Systems and methods for providing sensitive and specific alarms

ABSTRACT

Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/720,286, filed Oct. 30, 2012, the disclosure of which is herebyexpressly incorporated by reference in its entirety and is herebyexpressly made a portion of this application.

BACKGROUND

The present systems and methods relate to processing analyte sensor datafrom a continuous analyte sensor. More particularly, the present systemsand methods relate to providing accurate predictive alarms to a user.

DESCRIPTION OF RELATED ART

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which can cause anarray of physiological derangements associated with the deterioration ofsmall blood vessels, for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye. A hypoglycemic reaction (lowblood sugar) can be induced by an inadvertent overdose of insulin, orafter a normal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricks to obtain blood samples for measurement. Due to the lack ofcomfort and convenience associated with finger pricks, a person withdiabetes normally only measures his or her glucose levels two to fourtimes per day. Unfortunately, time intervals between measurements can bespread far enough apart that the person with diabetes finds out too lateof a hyperglycemic or hypoglycemic condition, sometimes incurringdangerous side effects. It is not only unlikely that a person withdiabetes will take a timely SMBG value, it is also likely that he or shewill not know if his or her blood glucose value is going up (higher) ordown (lower) based on conventional methods. Diabetics thus may beinhibited from making educated insulin therapy decisions.

Another device that some diabetics use to monitor their blood glucose isa continuous analyte sensor. A continuous analyte sensor typicallyincludes a sensor that is placed subcutaneously, transdermally (e.g.,transcutaneously), or intravascularly. The sensor measures theconcentration of a given analyte within the body, and generates a signalthat is transmitted to electronics associated with the sensor.

One of the major perceived benefits of a continuous analyte sensor isthe ability of these devices to have alarms to alert the diabetic userof hyperglycemic or hypoglycemic events before they occur. Additionally,it is desirable for these alarms to be accurate, so that a user does notbecome de-sensitized from their alarm going off, such that they ignoreit because it has become a nuisance. The present disclosure addressesthese needs.

SUMMARY

The present systems and methods relate to processing analyte sensordata. Provided herein are systems and methods that allow a user toreceive alerts or alarms indicative of glycemic condition in a moreaccurate and useful way. Consequently, the user may have an improveduser experience using such systems and methods.

In a first aspect, a method of activating a hypoglycemic indicator basedon continuous glucose sensor data is provided, the method comprising:evaluating sensor data using a first function to determine whether areal time glucose value meets one or more first criteria; evaluatingsensor data using a second function to determine whether a predictedglucose value meets one or more second criteria; activating ahypoglycemic indicator if either the one or more first criteria or theone or more second criteria are met; and providing an output based onthe activated hypoglycemic indicator. In an embodiment of the firstaspect, the evaluating sensor data using a first function to determinewhether a real time glucose value meets one or more first criteriacomprises determining whether the real-time glucose value passes aglucose threshold. In an embodiment of the first aspect, the evaluatingsensor data using a first function to determine whether a real timeglucose value meets one or more first criteria further comprisesdetermining whether an amplitude of rate of change or direction of rateof change meets a rate of change criterion. In an embodiment of thefirst aspect, the evaluating sensor data using a first function todetermine whether the real time glucose value meets one or more firstcriteria comprises evaluating a static risk of a substantially real timeglucose value. In an embodiment of the first aspect, the evaluatingsensor data using a second function to determine whether the predictedglucose value meets one or more second criteria comprises evaluating adynamic risk of the predicted glucose value. In an embodiment of thefirst aspect, the second function comprises an artificial neural networkmodel that uses at least one of exercise, stress, illness or surgery todetermine the predicted glucose value. In an embodiment of the firstaspect, the second function utilizes a first order autoregressive modelto determine the predicted glucose value. In an embodiment of the firstaspect, the first order autoregressive model comprises a parameteralpha, and wherein alpha is estimated recursively each time a sensordata points is received. In an embodiment of the first aspect, the firstorder autoregressive model comprises a forgetting factor, a predictionhorizon and a prediction threshold tuned to provide no more than oneadditional alarm per week based on a retrospective analysis comparingthe use of the first function and the second function together ascompared to the first function alone. In an embodiment of the firstaspect, evaluating the sensor data using the first function and thesecond function allows for increased warning time of hypoglycemic alertswithout substantially increasing the number of alerts as compared toevaluating the sensor data using the first function without evaluatingthe sensor data using the second function. In an embodiment of the firstaspect, the second function comprises a Kalman Filter to determine thepredicted glucose value using as an input an estimate of the rate ofchange of the blood glucose. In an embodiment of the first aspect, theone or more first criteria comprises a first threshold that isconfigured to be user settable. In an embodiment of the first aspect,the one or more second criteria comprises a second threshold that is notuser settable. In an embodiment of the first aspect, the second functioncomprises a prediction horizon that is not user settable. In anembodiment of the first aspect, the one or more second criteriacomprises a second threshold that is adaptively set by the processormodule based on the first threshold. In an embodiment of the firstaspect, the second function comprises a prediction horizon that isadaptively set by the processor module based on the first threshold. Inan embodiment of the first aspect, the hypoglycemic indicator comprisesa flag that has a particular set of instructions associated with itdepending on whether the hypoglycemic indicator was activated based onthe first function meeting the one or more first criteria or whether thehypoglycemic indicator was activated based on the second functionmeeting the one or more second criteria. In an embodiment of the firstaspect, the output comprises at least one of an audible, tactile orvisual output, and wherein the output is differentiated and/or providesinformation selectively based on whether the hypoglycemic indicator wasactivated based on the first function meeting the one or more firstcriteria or whether the hypoglycemic indicator was activated based onthe second function meeting the one or more second criteria. In anembodiment of the first aspect, providing an output comprisestransmitting a message to an insulin delivery device includinginstructions associated with at least one of: a) suspending insulindelivery, b) initiating a hypoglycemia and/or hyperglycemia minimizeralgorithm, c) controlling insulin delivery or d) information associatedwith the hypoglycemia indicator.

In a second aspect, a system for processing data is provided, the systemcomprising: a continuous analyte sensor configured to be implantedwithin a body; and sensor electronics configured to receive and processsensor data output by the sensor, the sensor electronics including aprocessor configured to: evaluate sensor data using a first function todetermine whether a real time glucose value meets one or more firstcriteria; evaluate sensor data using a second function to determinewhether a predicted glucose value meets one or more second criteria;activate a hypoglycemic indicator if either the one or more firstcriteria or the one or more second criteria are met; and provide anoutput based on the activated hypoglycemic indicator. In an embodimentof the second aspect, the evaluating sensor data using a first functionto determine whether a real time glucose value meets one or more firstcriteria comprises determining whether the real-time glucose valuepasses a glucose threshold. In an embodiment of the second aspect, theevaluating sensor data using a first function to determine whether areal time glucose value meets one or more first criteria furthercomprises determining whether an amplitude of rate of change ordirection of rate of change meets a rate of change criterion. In anembodiment of the second aspect, the evaluating sensor data using afirst function to determine whether the real time glucose value meetsone or more first criteria comprises evaluating a static risk of asubstantially real time glucose value. In an embodiment of the secondaspect, the evaluating sensor data using a second function to determinewhether the predicted glucose value meets one or more second criteriacomprises evaluating a dynamic risk of the predicted glucose value. Inan embodiment of the second aspect, the second function comprises anartificial neural network model that uses at least one of exercise,stress, illness or surgery to determine the predicted glucose value. Inan embodiment of the second aspect, the second function utilizes a firstorder autoregressive model to determine the predicted glucose value. Inan embodiment of the second aspect, the first order autoregressive modelcomprises a parameter alpha, and wherein alpha is estimated recursivelyeach time a sensor data points is received. In an embodiment of thesecond aspect, the first order autoregressive model comprises aforgetting factor, a prediction horizon and a prediction threshold tunedto provide no more than one additional alarm per week based on aretrospective analysis comparing the use of the first function and thesecond function together as compared to the first function alone. In anembodiment of the second aspect, evaluating the sensor data using thefirst function and the second function allows for increased warning timeof hypoglycemic alerts without substantially increasing the number ofalerts as compared to evaluating the sensor data using the firstfunction without evaluating the sensor data using the second function.In an embodiment of the second aspect, the second function comprises aKalman Filter to determine the predicted glucose value using as an inputan estimate of the rate of change of the blood glucose. In an embodimentof the second aspect, the one or more first criteria comprises a firstthreshold that is configured to be user settable. In an embodiment ofthe second aspect, the one or more second criteria comprises a secondthreshold that is not user settable. In an embodiment of the secondaspect, the second function comprises a prediction horizon that is notuser settable. In an embodiment of the second aspect, the one or moresecond criteria comprises a second threshold that is adaptively set bythe processor module based on the first threshold. In an embodiment ofthe second aspect, the second function comprises a prediction horizonthat is adaptively set by the processor module based on the firstthreshold. In an embodiment of the second aspect, the hypoglycemicindicator comprises a flag that has a particular set of instructionsassociated with it depending on whether the hypoglycemic indicator wasactivated based on the first function meeting the one or more firstcriteria or whether the hypoglycemic indicator was activated based onthe second function meeting the one or more second criteria. In anembodiment of the second aspect, the output comprises at least one of anaudible, tactile or visual output, and wherein the output isdifferentiated and/or provides information selectively based on whetherthe hypoglycemic indicator was activated based on the first functionmeeting the one or more first criteria or whether the hypoglycemicindicator was activated based on the second function meeting the one ormore second criteria. In an embodiment of the second aspect, providingoutput comprises transmitting a message to an insulin delivery deviceincluding instructions associated with at least one of: a) suspendinginsulin delivery, b) initiating a hypoglycemia and/or hyperglycemiaminimizer algorithm, c) controlling insulin delivery or d) informationassociated with the hypoglycemia indicator.

In a third aspect, a method of transitioning between states associatedwith a host's glycemic condition is provided, the method comprising:evaluating sensor data from a continuous glucose sensor and activatingan alert state based on the sensor data meeting one or more activetransition criteria associated with a hypoglycemic condition orhyperglycemic condition; providing an output associated with the activealert state, wherein the output is indicative of the hypoglycemiccondition or hyperglycemic condition; transitioning from the activestate to an acknowledged state for a time period responsive to at leastone of a user acknowledgment of the alert state or data indicative ofthe host's glucose trending toward euglycemia; actively monitoring dataassociated with the host's hypoglycemic or hyperglycemic condition for atime period in the acknowledged state; and transitioning from theacknowledged state to at least one of an inactive state or active stateresponsive to the data associated with the host's hypoglycemic orhyperglycemic condition meeting one or more predetermined criteria. Inan embodiment of the third aspect, the transitioning from the activestate to an acknowledged state comprises transitioning from the activestate to the acknowledged state based on the data indicative of thehost's glucose trending toward euglycemia, wherein the data is selectedfrom the group consisting of a) sensor data indicative of a change inglucose trend or b) insulin information associated with a correction ofthe condition. In an embodiment of the third aspect, the transitioningfrom the active state to an acknowledged state comprises transitioningfrom the active state to the acknowledged state based on the a useracknowledgment, wherein the data is selected from the group consistingof a) a user acknowledgement of the alert on a user interface or b) userinput insulin information or c) user input of meal information. In anembodiment of the third aspect, the actively monitoring comprisesmonitoring at least one of the sensor data, sensor diagnosticinformation, meal information, insulin information, or eventinformation. In an embodiment of the third aspect, transitioning fromthe acknowledged state comprises transitioning from the acknowledgedstate to the inactive state based on the sensor data no longer meetingthe one or more criteria associated with a hypoglycemic condition orhyperglycemic condition. In an embodiment of the third aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the inactive state based on the sensor datameeting one or more inactive transition criteria, wherein theinactivation criteria are different from the one or more activetransition criteria associated with a hypoglycemic condition orhyperglycemic condition. In an embodiment of the third aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the inactive state based on insulin dataand/or meal information. In an embodiment of the third aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the active state based on the one or moreactive transition criteria associated with a hypoglycemic condition orhyperglycemic condition being met and based on an expiration of apredetermined time period. In an embodiment of the third aspect, afterreceiving the user acknowledgement of the alert state and determiningdata is indicative of the host's glucose trending toward euglycemia,further comprising transitioning from the acknowledged state to theactive state based on the host's glucose trending away from euglycemiaduring the active monitoring time period. In an embodiment of the thirdaspect, the method further comprises selectively outputting informationassociated with the state transition. In an embodiment of the thirdaspect, the output associated with a transition to the active state isdifferent from the output association with a transition from theacknowledged state to the inactive state.

In a fourth aspect, a system for processing data is provided, the systemcomprising: a continuous analyte sensor configured to be implantedwithin a body; and sensor electronics configured to receive and processsensor data output by the sensor, the sensor electronics including aprocessor configured to: evaluate sensor data from a continuous glucosesensor and activating an alert state based on the sensor data meetingone or more active transition criteria associated with a hypoglycemiccondition or hyperglycemic condition; provide an output associated withthe active alert state, wherein the output is indicative of thehypoglycemic condition or hyperglycemic condition; transition from theactive state to an acknowledged state for a time period responsive to atleast one of a user acknowledgment of the alert state or data indicativeof the host's glucose trending toward euglycemia; actively monitor dataassociated with the host's hypoglycemic or hyperglycemic condition for atime period in the acknowledged state; and transition from theacknowledged state to at least one of an inactive state or active stateresponsive to the data associated with the host's hypoglycemic orhyperglycemic condition meeting one or more predetermined criteria. Inan embodiment of the fourth aspect, the transitioning from the activestate to an acknowledged state comprises transitioning from the activestate to the acknowledged state based on the data indicative of thehost's glucose trending toward euglycemia, wherein the data is selectedfrom the group consisting of a) sensor data indicative of a change inglucose trend or b) insulin information associated with a correction ofthe condition. In an embodiment of the fourth aspect, transitioning fromthe active state to an acknowledged state comprises transitioning fromthe active state to the acknowledged state based on the a useracknowledgment, wherein the data is selected from the group consistingof a) a user acknowledgement of the alert on a user interface or b) userinput insulin information or c) user input of meal information. In anembodiment of the fourth aspect, the actively monitoring comprisesmonitoring at least one of the sensor data, sensor diagnosticinformation, meal information, insulin information, or eventinformation. In an embodiment of the fourth aspect, transitioning fromthe acknowledged state comprises transitioning from the acknowledgedstate to the inactive state based on the sensor data no longer meetingthe one or more criteria associated with a hypoglycemic condition orhyperglycemic condition. In an embodiment of the fourth aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the inactive state based on the sensor datameeting one or more inactive transition criteria, wherein theinactivation criteria are different from the one or more activetransition criteria associated with a hypoglycemic condition orhyperglycemic condition. In an embodiment of the fourth aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the inactive state based on insulin dataand/or meal information. In an embodiment of the fourth aspect,transitioning from the acknowledged state comprises transitioning fromthe acknowledged state to the active state based on the one or moreactive transition criteria associated with a hypoglycemic condition orhyperglycemic condition being met and based on an expiration of apredetermined time period. In an embodiment of the fourth aspect, afterreceiving the user acknowledgement of the alert state and determiningdata is indicative of the host's glucose trending toward euglycemia,further comprising transitioning from the acknowledged state to theactive state based on the host's glucose trending away from euglycemiaduring the active monitoring time period. In an embodiment of the fourthaspect, the system further comprises selectively outputting informationassociated with the state transition. In an embodiment of the fourthaspect, the output associated with a transition to the active state isdifferent from the output association with a transition from theacknowledged state to the inactive state.

In a fifth aspect, a method of determining when to re-alert a user basedafter a user has acknowledged a first alert is provided, the methodcomprising: initially activating an alert state based on one or morecriteria based on data associated with a hypoglycemic or hyperglycemiccondition being met; transitioning to an acknowledged state for anpredetermined active monitoring time period responsive to at least oneof a user acknowledgment or data indicative of the host's glucosetrending toward euglycemia; actively monitoring, by a processor module,data associated with the host's hypoglycemic or hyperglycemic conditionduring the active monitoring time period; and reactivating the firstalert state during the acknowledgement time period initiated by the dataassociated with the host's hypoglycemic or hyperglycemic conditionmeeting one or more second criteria. In an embodiment of the fifthaspect, the second one or more criteria are different from the first oneor more criteria. In an embodiment of the fifth aspect, the methodfurther comprises providing a first output associated with the initiallyactivating and providing a second output associated with thereactivating. In an embodiment of the fifth aspect, the first output andthe second output are different. In an embodiment of the fifth aspect,the second criteria comprise conditions indicative of the host's glucosetrending toward euglycemia and further comprise conditions indicative ofthe host's glucose trending away from euglycemia after trending towardeuglycemia during the active monitoring time period. In an embodiment ofthe fifth aspect, the one or more second criteria associated withreactivation comprise a change in a real-time glucose value as comparedto the real-time glucose value associated with the initially activating.

In a sixth aspect, a system for processing data is provided, the systemcomprising: a continuous analyte sensor configured to be implantedwithin a body; and sensor electronics configured to receive and processsensor data output by the sensor, the sensor electronics including aprocessor configured to: initially activate an alert state based on oneor more criteria based on data associated with a hypoglycemic orhyperglycemic condition being met; transition to an acknowledged statefor an predetermined active monitoring time period responsive to atleast one of a user acknowledgment or data indicative of the host'sglucose trending toward euglycemia; actively monitor data associatedwith the host's hypoglycemic or hyperglycemic condition during theactive monitoring time period; and reactivate the first alert stateduring the acknowledgement time period initiated by the data associatedwith the host's hypoglycemic or hyperglycemic condition meeting one ormore second criteria. In an embodiment of the sixth aspect, the secondone or more criteria are different from the first one or more criteria.In an embodiment of the sixth aspect, the system further comprisesproviding a first output associated with the initially activating andproviding a second output associated with the reactivating. In anembodiment of the sixth aspect, the first output and the second outputare different. In an embodiment of the sixth aspect, the second criteriacomprise conditions indicative of the host's glucose trending towardeuglycemia and further comprise conditions indicative of the host'sglucose trending away from euglycemia after trending toward euglycemiaduring the active monitoring time period. In an embodiment of the sixthaspect, the one or more second criteria associated with reactivationcomprise a change in a real-time glucose value as compared to thereal-time glucose value associated with the initially activating.

In a seventh aspect, a method of avoiding unnecessary hyperglycemicalerts is provided, the method comprising: initially activating a firstalert state based on one or more first criteria associated with ahyperglycemic condition; waiting a time period before providing anoutput associated with the first alert state; actively monitoring, by aprocessor module, data associated with the host's hyperglycemiccondition during the waiting time period; and providing an outputassociated with the first alert state after the waiting time periodbased on the data associated with the host's hyperglycemic conditionmeeting one or more second criteria. In an embodiment of the seventhaspect, the actively monitoring comprises determining an average glucoseover a window of time. In an embodiment of the seventh aspect, theactively monitoring comprises determining an amplitude and/or directionof rate of change. In an embodiment of the seventh aspect, the activelymonitoring comprises determining an amplitude and/or direction of rateof acceleration. In an embodiment of the seventh aspect, the activelymonitoring comprises evaluating insulin information. In an embodiment ofthe seventh aspect, the actively monitoring comprises evaluating mealinformation or timing. In an embodiment of the seventh aspect, thewaiting time period is user selectable. In an embodiment of the seventhaspect, the method further comprises not providing output associatedwith the first alert state after the waiting time period based on thedata associated with the host's hyperglycemic condition not meeting theone or more second criteria. In an embodiment of the seventh aspect, theone or more first criteria and the one or more second criteria are thesame. In an embodiment of the seventh aspect, the one or more firstcriteria and the one or more second criteria are different. In anembodiment of the seventh aspect, the method further comprisestransitioning from the first alert state to an inactive alert statebased on the data associated with the host's hyperglycemic conditionmeeting one or more third criteria. In an embodiment of the seventhaspect, the one or more first criteria and the one or more thirdcriteria are the same. In an embodiment of the seventh aspect, the oneor more first criteria and the one or more third criteria are different.

In an eighth aspect, a system for processing data is provided, thesystem comprising: a continuous analyte sensor configured to beimplanted within a body; and sensor electronics configured to receiveand process sensor data output by the sensor, the sensor electronicsincluding a processor configured to: initially activate a first alertstate based on one or more first criteria associated with ahyperglycemic condition; wait a time period before providing an outputassociated with the first alert state; actively monitor data associatedwith the host's hyperglycemic condition during the waiting time period;and provide an output associated with the first alert state after thewaiting time period based on the data associated with the host'shyperglycemic condition meeting one or more second criteria. In anembodiment of the eighth aspect, the actively monitoring comprisesdetermining an average glucose over a window of time. In an embodimentof the eighth aspect, the actively monitoring comprises determining anamplitude and/or direction of rate of change. In an embodiment of theeighth aspect, the actively monitoring comprises determining anamplitude and/or direction of rate of acceleration. In an embodiment ofthe eighth aspect, the actively monitoring comprises evaluating insulininformation. In an embodiment of the eighth aspect, the activelymonitoring comprises evaluating meal information or timing. In anembodiment of the eighth aspect, the waiting time period is userselectable. In an embodiment of the eighth aspect, the system furthercomprises not providing output associated with the first alert stateafter the waiting time period based on the data associated with thehost's hyperglycemic condition not meeting the one or more secondcriteria. In an embodiment of the eighth aspect, the one or more firstcriteria and the one or more second criteria are the same. In anembodiment of the eighth aspect, the one or more first criteria and theone or more second criteria are different. In an embodiment of theeighth aspect, the system further comprises transitioning from the firstalert state to an inactive alert state based on the data associated withthe host's hyperglycemic condition meeting one or more third criteria.In an embodiment of the eighth aspect, the one or more first criteriaand the one or more third criteria are the same. In an embodiment of theeighth aspect, the one or more first criteria and the one or more thirdcriteria are different.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present disclosure, both as to its structure andoperation, may be understood in part by study of the accompanyingdrawings, in which like reference numerals refer to like parts. Thedrawings are not necessarily to scale, emphasis instead being placedupon illustrating the principles of the disclosure.

FIG. 1 is a schematic view of a continuous analyte sensor systemattached to a host and communicating with a plurality of exampledevices.

FIG. 2 is a block diagram that illustrates electronics associated withthe sensor system of FIG. 1.

FIG. 3A illustrates an embodiment, where the receiver of FIG. 1 shows anumeric representation of the estimated analyte value on its userinterface.

FIG. 3B illustrates an embodiment, where the receiver of FIG. 1 shows anestimated glucose value and one hour of historical data trend on itsuser interface.

FIG. 3C illustrates an embodiment, where the receiver of FIG. 1 shows anestimated glucose value and three hours of historical trend data on itsuser interface.

FIG. 3D illustrates an embodiment, where the receiver of FIG. 1 showsand estimated glucose value and nine hours of historical trend data onits user interface.

FIGS. 4A, 4B and 4C are illustrations of receiver liquid crystaldisplays showing embodiments of screen displays.

FIG. 4D is a screenshot of a smartphone display illustrating oneembodiment of an alert indicting that the user's blood glucose isdropping and will soon be in a low range.

FIG. 4E is a screenshot of a smartphone display illustrating oneembodiment of a blood glucose trend graph.

FIG. 4F is an embodiment of a blood glucose trend arrow.

FIG. 5 is an illustration of a continuous trace of glucose valuesmeasured during a time frame in accordance with an embodiment of thedisclosure.

FIG. 6 is a flowchart illustrating a process for dynamically andintelligently providing a prediction alert/alarm in accordance with anembodiment of the disclosure.

FIG. 7 is an illustration of a continuous trace of glucose valuesmeasured during a time frame in accordance with an embodiment of thedisclosure.

FIG. 8 is a flowchart illustrating a process for dynamically andintelligently monitoring the status after an alert/alarm has beentriggered in accordance with an embodiment of the disclosure.

FIG. 9 is a flowchart illustrating a process for determining a statuschange in accordance with an embodiment of the disclosure.

FIG. 10 is a flowchart illustrating a process for determining if areactivation condition is met in accordance with an embodiment of thedisclosure.

FIG. 11 is an illustration of a state diagram showing the transitionsfrom various states in accordance with an embodiment of the disclosure.

FIGS. 12-16 are example graphs showing estimated glucose values (“EGVs”)and when alerts would be expected to be provided for the EGVs inaccordance with embodiments of the disclosure.

DETAILED DESCRIPTION

The following detailed description describes the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

Sensor System and Applicator

FIG. 1 depicts an example system 100, in accordance with some exampleimplementations. The system 100 includes a continuous analyte sensorsystem 8 including sensor electronics 12 and a continuous analyte sensor10. The system 100 may include other devices and/or sensors, such asmedicament delivery pump 2 and glucose meter 4. The continuous analytesensor 10 may be physically connected to sensor electronics 12 and maybe integral with (e.g., non-releasably attached to) or releasablyattachable to the continuous analyte sensor 10. The sensor electronics12, medicament delivery pump 2, and/or glucose meter 4 may couple withone or more devices, such as display devices 14, 16, 18, and/or 20.

In some example implementations, the system 100 may include acloud-based analyte processor 490 configured to analyze analyte data(and/or other patient related data) provided via network 406 (e.g., viawired, wireless, or a combination thereof) from sensor system 8 andother devices, such as display devices 14-20 and the like, associatedwith the host (also referred to as a patient) and generate reportsproviding high-level information, such as statistics, regarding themeasured analyte over a certain time frame. A full discussion of using acloud-based analyte processing system may be found in U.S. PatentApplication No. 61/655,991, entitled “Cloud-Based Processing of AnalyteData” and filed on Jun. 5, 2012, herein incorporated by reference in itsentirety.

In some example implementations, the sensor electronics 12 may includeelectronic circuitry associated with measuring and processing datagenerated by the continuous analyte sensor 10. This generated continuousanalyte sensor data may also include algorithms, which can be used toprocess and calibrate the continuous analyte sensor data, although thesealgorithms may be provided in other ways as well. The sensor electronics12 may include hardware, firmware, software, or a combination thereof toprovide measurement of levels of the analyte via a continuous analytesensor, such as a continuous glucose sensor. An example implementationof the sensor electronics 12 is described further below with respect toFIG. 2.

The term “sensor data”, as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any dataassociated with a sensor, such as a continuous analyte sensor. Sensordata includes a raw data stream, or simply a data stream, of analog ordigital signal related to a measured analyte from an analyte sensor (orother signal received from another sensor), as well as calibrated and/orfiltered raw data. In one example, the sensor data comprises digitaldata in “counts” converted by an A/D converter from an analog signal(e.g., voltage or amps) and includes one or more data pointsrepresentative of a glucose concentration. Thus, the terms “sensor datapoint” and “data point” refer generally to a digital representation ofsensor data at a particular time. The term broadly encompasses aplurality of time spaced data points from a sensor, such as from asubstantially continuous glucose sensor, which includes individualmeasurements taken at time intervals ranging from fractions of a secondup to, e.g., 1, 2, or 5 minutes or longer. In another example, thesensor data includes an integrated digital value representative of oneor more data points averaged over a time period. In some embodiments,sensor data may include calibrated data, smoothed data, filtered data,transformed data, and/or any other data associated with a sensor.

The sensor electronics 12 may, as noted, couple (e.g., wirelessly andthe like) with one or more devices, such as display devices 14, 16, 18,and/or 20. The display devices 14, 16, 18, and/or 20 may be configuredfor presenting information (and/or alarming), such as sensor informationtransmitted by the sensor electronics 12 for display at the displaydevices 14, 16, 18, and/or 20.

The display devices may include a relatively small, key fob-like displaydevice 14, a relatively large, hand-held display device 16, a cellularphone 18 (e.g., a smart phone, a tablet, and the like), a computer 20,and/or any other user equipment configured to at least presentinformation (e.g., medicament delivery information, discreteself-monitoring glucose readings, heart rate monitor, caloric intakemonitor, and the like).

In some example implementations, the relatively small, key fob-likedisplay device 14 may comprise a wrist watch, a belt, a necklace, apendent, a piece of jewelry, an adhesive patch, a pager, a key fob, aplastic card (e.g., credit card), an identification (ID) card, and/orthe like. This small display device 14 may include a relatively smalldisplay (e.g., smaller than the large display device 16) and may beconfigured to display certain types of displayable sensor information,such as a numerical value and an arrow.

In some example implementations, the relatively large, hand-held displaydevice 16 may comprise a hand-held receiver device, a palm-top computer,and/or the like. This large display device may include a relativelylarger display (e.g., larger than the small display device 14) and maybe configured to display information, such as a graphical representationof the continuous sensor data including current and historic sensor dataoutput by sensor system 8.

In some example implementations, the continuous analyte sensor 10comprises a sensor for detecting and/or measuring analytes, and thecontinuous analyte sensor 10 may be configured to continuously detectand/or measure analytes as a non-invasive device, a subcutaneous device,a transdermal device, and/or an intravascular device. In some exampleimplementations, the continuous analyte sensor 10 may analyze aplurality of intermittent blood samples, although other analytes may beused as well.

In some example implementations, the continuous analyte sensor 10 maycomprise a glucose sensor configured to measure glucose in the bloodusing one or more measurement techniques, such as enzymatic, chemical,physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, immunochemical, and the like.In implementations in which the continuous analyte sensor 10 includes aglucose sensor, the glucose sensor may be comprise any device capable ofmeasuring the concentration of glucose and may use a variety oftechniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescence monitoring), toprovide a data, such as a data stream, indicative of the concentrationof glucose in a host. The data stream may be raw data signal, which isconverted into a calibrated and/or filtered data stream used to providea value of glucose to a host, such as a user, a patient, or a caretaker(e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse,or any other individual that has an interest in the wellbeing of thehost). Moreover, the continuous analyte sensor 10 may be implanted as atleast one of the following types of sensors: an implantable glucosesensor, a transcutaneous glucose sensor, implanted in a host vessel orextracorporeally, a subcutaneous sensor, a refillable subcutaneoussensor, an intravascular sensor.

Although the disclosure herein refers to some implementations thatinclude a continuous analyte sensor 10 comprising a glucose sensor, thecontinuous analyte sensor 10 may comprises other types of analytesensors as well. Moreover, although some implementations refer to theglucose sensor as an implantable glucose sensor, other types of devicescapable of detecting a concentration of glucose and providing an outputsignal representative of glucose concentration may be used as well.Furthermore, although the description herein refers to glucose as theanalyte being measured, processed, and the like, other analytes may beused as well including, for example, ketone bodies (e.g., acetone,acetoacetic acid and beta hydroxybutyric acid, lactate, etc.), glucagon,Acetyl Co A, triglycerides, fatty acids, intermediaries in the citricacid cycle, choline, insulin, cortisol, testosterone, and the like.

FIG. 2 depicts an example of sensor electronics 12, in accordance withsome example implementations. The sensor electronics 12 may includesensor electronics that are configured to process sensor information,such as sensor data, and generate transformed sensor data anddisplayable sensor information, e.g., via a processor module. Forexample, the processor module may transform sensor data into one or moreof the following: filtered sensor data (e.g., one or more filteredanalyte concentration values), raw sensor data, calibrated sensor data(e.g., one or more calibrated analyte concentration values), rate ofchange information, trend information, rate of acceleration/decelerationinformation, sensor diagnostic information, location information,alarm/alert information, calibration information, smoothing and/orfiltering algorithms of sensor data, and/or the like.

In some embodiments, a processor module 214 is configured to achieve asubstantial portion, if not all, of the data processing. Processormodule 214 may be integral to sensor electronics 12 and/or may belocated remotely, such as in one or more of devices 14, 16, 18, and/or20 and/or cloud 490. In some embodiments, processor module 214 maycomprise a plurality of smaller subcomponents or submodules. Forexample, processor module 214 may include an alert module (not shown) orprediction module (not shown), or any other suitable module that may beutilized to efficiently process data. When processor module 214 is madeup of a plurality of submodules, the submodules may be located withinprocessor module 214, including within the sensor electronic 12 or otherassociated devices (e.g., 14, 16, 18, 20 and/or 490). For example, insome embodiments, processor module 214 may be located at least partiallywithin cloud-based analyte processor 490 or elsewhere in network 406.

In some example implementations, the processor module 214 may beconfigured to calibrate the sensor data, and the data storage memory 220may store the calibrated sensor data points as transformed sensor data.Moreover, the processor module 214 may be configured, in some exampleimplementations, to wirelessly receive calibration information from adisplay device, such as devices 14, 16, 18, and/or 20, to enablecalibration of the sensor data from sensor 12. Furthermore, theprocessor module 214 may be configured to perform additional algorithmicprocessing on the sensor data (e.g., calibrated and/or filtered dataand/or other sensor information), and the data storage memory 220 may beconfigured to store the transformed sensor data and/or sensor diagnosticinformation associated with the algorithms.

In some example implementations, the sensor electronics 12 may comprisean application-specific integrated circuit (ASIC) 205 coupled to a userinterface 222. The ASIC 205 may further include a potentiostat 210, atelemetry module 232 for transmitting data from the sensor electronics12 to one or more devices, such devices 14, 16, 18, and/or 20, and/orother components for signal processing and data storage (e.g., processormodule 214 and data storage memory 220). Although FIG. 2 depicts ASIC205, other types of circuitry may be used as well, including fieldprogrammable gate arrays (FPGA), one or more microprocessors configuredto provide some (if not all of) the processing performed by the sensorelectronics 12, analog circuitry, digital circuitry, or a combinationthereof.

In the example depicted at FIG. 2, the potentiostat 210 is coupled to acontinuous analyte sensor 10, such as a glucose sensor to generatesensor data from the analyte. The potentiostat 210 may also provide viadata line 212 a voltage to the continuous analyte sensor 10 to bias thesensor for measurement of a value (e.g., a current and the like)indicative of the analyte concentration in a host (also referred to asthe analog portion of the sensor). The potentiostat 210 may have one ormore channels depending on the number of working electrodes at thecontinuous analyte sensor 10.

In some example implementations, the potentiostat 210 may include aresistor that translates a current value from the sensor 10 into avoltage value, while in some example implementations, acurrent-to-frequency converter (not shown) may also be configured tointegrate continuously a measured current value from the sensor 10using, for example, a charge-counting device. In some exampleimplementations, an analog-to-digital converter (not shown) may digitizethe analog signal from the sensor 10 into so-called “counts” to allowprocessing by the processor module 214. The resulting counts may bedirectly related to the current measured by the potentiostat 210, whichmay be directly related to an analyte level, such as a glucose level, inthe host.

The telemetry module 232 may be operably connected to processor module214 and may provide the hardware, firmware, and/or software that enablewireless communication between the sensor electronics 12 and one or moreother devices, such as display devices, processors, network accessdevices, and the like. A variety of wireless radio technologies that canbe implemented in the telemetry module 232 include Bluetooth, BluetoothLow-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radioaccess technologies, radio frequency (RF), infrared (IR), paging networkcommunication, magnetic induction, satellite data communication, spreadspectrum communication, frequency hopping communication, near fieldcommunications, and/or the like. In some example implementations, thetelemetry module 232 comprises a Bluetooth chip, although the Bluetoothtechnology may also be implemented in a combination of the telemetrymodule 232 and the processor module 214.

The processor module 214 may control the processing performed by thesensor electronics 12. For example, the processor module 214 may beconfigured to process data (e.g., counts), from the sensor, filter thedata, calibrate the data, perform fail-safe checking, and/or the like.

In some example implementations, the processor module 214 may comprise adigital filter, such as for example an infinite impulse response (IIR)or a finite impulse response (FIR) filter. This digital filter maysmooth a raw data stream received from sensor 10. Generally, digitalfilters are programmed to filter data sampled at a predetermined timeinterval (also referred to as a sample rate). In some exampleimplementations, such as when the potentiostat 210 is configured tomeasure the analyte (e.g., glucose and/or the like) at discrete timeintervals, these time intervals determine the sampling rate of thedigital filter. In some example implementations, the potentiostat 210may be configured to measure continuously the analyte, for example,using a current-to-frequency converter. In these current-to-frequencyconverter implementations, the processor module 214 may be programmed torequest, at predetermined time intervals (acquisition time), digitalvalues from the integrator of the current-to-frequency converter. Thesedigital values obtained by the processor module 214 from the integratormay be averaged over the acquisition time due to the continuity of thecurrent measurement. As such, the acquisition time may be determined bythe sampling rate of the digital filter.

The processor module 214 may further include a data generator (notshown) configured to generate data packages for transmission to devices,such as the display devices 14, 16, 18, and/or 20. Furthermore, theprocessor module 214 may generate data packets for transmission to theseoutside sources via telemetry module 232. In some exampleimplementations, the data packages may, as noted, be customizable foreach display device, and/or may include any available data, such as atime stamp, displayable sensor information, transformed sensor data, anidentifier code for the sensor and/or sensor electronics 12, raw data,filtered data, calibrated data, rate of change information, trendinformation, error detection or correction, and/or the like.

The processor module 214 may also include a program memory 216 and othermemory 218. The processor module 214 may be coupled to a communicationsinterface, such as a communication port 238, and a source of power, suchas a battery 234. Moreover, the battery 234 may be further coupled to abattery charger and/or regulator 236 to provide power to sensorelectronics 12 and/or charge the battery 234.

The program memory 216 may be implemented as a semi-static memory forstoring data, such as an identifier for a coupled sensor 10 (e.g., asensor identifier (ID)) and for storing code (also referred to asprogram code) to configure the ASIC 205 to perform one or more of theoperations/functions described herein. For example, the program code mayconfigure processor module 214 to process data streams or counts,filter, calibrate, perform fail-safe checking, and the like.

The memory 218 may also be used to store information. For example, theprocessor module 214 including memory 218 may be used as the system'scache memory, where temporary storage is provided for recent sensor datareceived from the sensor. In some example implementations, the memorymay comprise memory storage components, such as read-only memory (ROM),random-access memory (RAM), dynamic-RAM, static-RAM, non-static RAM,easily erasable programmable read only memory (EEPROM), rewritable ROMs,flash memory, and the like.

The data storage memory 220 may be coupled to the processor module 214and may be configured to store a variety of sensor information. In someexample implementations, the data storage memory 220 stores one or moredays of continuous analyte sensor data. For example, the data storagememory may store 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20,and/or 30 (or more days) of continuous analyte sensor data received fromsensor 10. The stored sensor information may include one or more of thefollowing: a time stamp, raw sensor data (one or more raw analyteconcentration values), calibrated data, filtered data, transformedsensor data, and/or any other displayable sensor information,calibration information (e.g., reference BG values and/or priorcalibration information), sensor diagnostic information, and the like.

The user interface 222 may include a variety of interfaces, such as oneor more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228,an audio transducer (e.g., speaker) 230, a backlight (not shown), and/orthe like. The components that comprise the user interface 222 mayprovide controls to interact with the user (e.g., the host). One or morebuttons 224 may allow, for example, toggle, menu selection, optionselection, status selection, yes/no response to on-screen questions, a“turn off” function (e.g., for an alarm), an “acknowledged” function(e.g., for an alarm), a reset, and/or the like. The LCD 226 may providethe user with, for example, visual data output. The audio transducer 230(e.g., speaker) may provide audible signals in response to triggering ofcertain alerts, such as present and/or predicted hyperglycemic andhypoglycemic conditions. In some example implementations, audiblesignals may be differentiated by tone, volume, duty cycle, pattern,duration, and/or the like. In some example implementations, the audiblesignal may be configured to be silenced (e.g., acknowledged or turnedoff) by pressing one or more buttons 224 on the sensor electronics 12and/or by signaling the sensor electronics 12 using a button orselection on a display device (e.g., key fob, cell phone, and/or thelike).

Although audio and vibratory alarms are described with respect to FIG.2, other alarming mechanisms may be used as well. For example, in someexample implementations, a tactile alarm is provided including a pokingmechanism configured to “poke” or physically contact the patient inresponse to one or more alarm conditions.

The battery 234 may be operatively connected to the processor module 214(and possibly other components of the sensor electronics 12) and providethe necessary power for the sensor electronics 12. In some exampleimplementations, the battery is a Lithium Manganese Dioxide battery,however any appropriately sized and powered battery can be used (e.g.,AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metalhydride, Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some example implementations, the battery isrechargeable. In some example implementations, a plurality of batteriescan be used to power the system. In yet other implementations, thereceiver can be transcutaneously powered via an inductive coupling, forexample.

A battery charger and/or regulator 236 may be configured to receiveenergy from an internal and/or external charger. In some exampleimplementations, a battery regulator (or balancer) 236 regulates therecharging process by bleeding off excess charge current to allow allcells or batteries in the sensor electronics 12 to be fully chargedwithout overcharging other cells or batteries. In some exampleimplementations, the battery 234 (or batteries) is configured to becharged via an inductive and/or wireless charging pad, although anyother charging and/or power mechanism may be used as well.

One or more communication ports 238, also referred to as externalconnector(s), may be provided to allow communication with other devices,for example a PC communication (com) port can be provided to enablecommunication with systems that are separate from, or integral with, thesensor electronics 12. The communication port, for example, may comprisea serial (e.g., universal serial bus or “USB”) communication port, andallow for communicating with another computer system (e.g., PC, personaldigital assistant or “PDA,” server, or the like). In some exampleimplementations, the sensor electronics 12 is able to transmithistorical data to a PC or other computing device (e.g., an analyteprocessor as disclosed herein) for retrospective analysis by a patientand/or physician.

In some continuous analyte sensor systems, an on-skin portion of thesensor electronics may be simplified to minimize complexity and/or sizeof on-skin electronics, for example, providing only raw, calibrated,and/or filtered data to a display device configured to run calibrationand other algorithms required for displaying the sensor data. However,the sensor electronics 12 (e.g., via processor module 214) may beimplemented to execute prospective algorithms used to generatetransformed sensor data and/or displayable sensor information,including, for example, algorithms that: evaluate a clinicalacceptability of reference and/or sensor data, evaluate calibration datafor best calibration based on inclusion criteria, evaluate a quality ofthe calibration, compare estimated analyte values with timecorresponding measured analyte values, analyze a variation of estimatedanalyte values, evaluate a stability of the sensor and/or sensor data,detect signal artifacts (noise), replace signal artifacts, determine arate of change and/or trend of the sensor data, perform dynamic andintelligent analyte value estimation, perform diagnostics on the sensorand/or sensor data, set modes of operation, evaluate the data foraberrancies, and/or the like.

Although separate data storage and program memories are shown in FIG. 2,a variety of configurations may be used as well. For example, one ormore memories may be used to provide storage space to support dataprocessing and storage requirements at sensor electronics 12.

Referring now to FIGS. 3A to 3D, more detailed schematic views ofhand-held receiver 16 is shown. Hand-held receiver 16 may comprisesystems necessary to receive, process, and display sensor data from ananalyte sensor, such as described elsewhere herein. Particularly, thehand-held receiver 16 can be a pager-sized device, for example, andcomprise a user interface that has a plurality of buttons 242 and aliquid crystal display (LCD) screen 244, and which can include abacklight. In some embodiments, the user interface can also include akeyboard, a speaker, and a vibrator.

In some embodiments, a user is able to toggle through some or all of thescreens shown in FIGS. 3A to 3D using a toggle button on the hand-heldreceiver. In some embodiments, the user is able to interactively selectthe type of output displayed on their user interface. In someembodiments, the sensor output can have alternative configurations.

In some embodiments, analyte values are displayed on e.g., a display ofmedical device received. In some embodiments, prompts or messages can bedisplayed on the display device to convey information to the user, suchas reference outlier values, requests for reference analyte values,therapy recommendations, deviation of the measured analyte values fromthe estimated analyte values, providing predictive alert/alarm,monitoring the glycemic alert state after the alert/alarm is triggered,determining state changes, or the like. Additionally, prompts can bedisplayed to guide the user through calibration or troubleshooting ofthe calibration.

FIGS. 4A to 4C illustrate some additional visual displays that can beprovided on the user interface 222. While these visual displays mayinclude the same or similar output to the ones shown on hand-held device16 in FIG. 3, and the visual displays of FIG. 4 can be provided on anysuitable user interface 222, such as those on devices 14, 16, 18, 20. Insome embodiments, the LCD 226 is a touch-activated screen, enabling eachselection by a user, for example, from a menu on the screen. Buttons canprovide for toggle, menu selection, option selection, mode selection,and reset, for example. In some alternative embodiments, a microphonecan be provided to allow for voice-activated control.

FIG. 4A is an illustration of an LCD screen 226 showing continuous andsingle point glucose information in the form of a trend graph 184 and asingle numerical value 186. The trend graph shows upper and lowerboundaries 182 representing a target range between which the host shouldmaintain his/her glucose values. Preferably, the receiver is configuredsuch that these boundaries 182 can be configured or customized by auser, such as the host or a care provider. By providing visualboundaries 182, in combination with continuous analyte values over time(e.g., a trend graph 184), a user can better learn how to controlhis/her analyte concentration (e.g., a person with diabetes can betterlearn how to control his/her glucose concentration) as compared tosingle point (e.g., single numerical value 186) alone. Although FIG. 4Aillustrates a 1-hour trend graph (e.g., depicted with a time range 188of 1-hour), a variety of time ranges can be represented on the screen226, for example, 3-hour, 9-hour, 1-day, and the like.

FIG. 4B is an illustration of an LCD screen 226 showing a low alertscreen that can be displayed responsive to a host's analyteconcentration falling below a lower boundary (see boundaries 182). Inthis example screen, a host's glucose concentration has fallen to 55mg/dL, which is below the lower boundary set in FIG. 4A, for example.The arrow 190 represents the direction of the analyte trend, forexample, indicating that the glucose concentration is continuing todrop. The annotation 192 (“LOW”) is helpful in immediately and clearlyalerting the host that his/her glucose concentration has dropped below apreset limit, and what may be considered to be a clinically safe value,for example.

In contrast, FIG. 4C is an illustration of an LCD screen 226 showing ahigh alert screen that can be displayed responsive to a host's analyteconcentration rising above an upper boundary (see boundaries 182). Inthis example screen, a host's glucose concentration has risen to 200mg/dL, which is above a boundary set by the host, thereby triggering thehigh alert screen. The arrow 190 represents the direction of the analytetrend, for example, indicating that the glucose concentration iscontinuing to rise. The annotation 192 (“HIGH”) is helpful inimmediately and clearly alerting the host that his/her glucoseconcentration has above a preset limit, and what may be considered to bea clinically safe value, for example.

Although a few example screens are depicted herein, a variety of screenscan be provided for illustrating any of the information described in theprovided embodiments, as well as additional information. A user cantoggle between these screens and/or the screens can be automaticallydisplayed responsive to programming within the e.g., hand-held receiver16, and can be simultaneously accompanied by another type of alert(e.g., audible or tactile).

For example, FIG. 4D is a screenshot of a smartphone 18 displayillustrating one embodiment of an alert indicting that the user's bloodglucose is dropping and will soon be in a low range. FIG. 4E is ascreenshot of a smartphone 18 display illustrating one embodiment of ablood glucose trend graph. FIG. 4F is an embodiment of a blood glucosetrend arrow.

In some embodiments, the processor module 214 may provide a predictivealert on a smartphone 18 display or user interface 222 when a severehypoglycemic event is predicted to occur in the near future. Forexample, the predictive alert may be shown when a severe hypoglycemicevent is predicted to occur within 5 minutes, 10 minutes, 15 minutes, 20minutes, 30 minutes, etc. With reference to FIG. 4D, an arrow 300 may bedisplayed on a trend screen pointing towards a BG value 302 thatindicates a severe hypoglycemic event, such as 55 mg/dL. The arrow 300may change color as it transitions from euglycemia to hypoglycemia, toemphasize the change in glucose levels that is expected. Furthermore,the arrow 300 may be animated to flash to emphasize the seriousness ofthe alert. The display may also show text 304, such as Going LOW. Thispredictive alert may be configured to be prioritized over (override)whatever mode or application the smartphone 18 is in at the time theprocessor module 214 determines that a severe hypoglycemic event ispredicted to occur. In other words, the alert may interrupt whatever iscurrently on the smartphone's user interface 222.

In these embodiments, the processor module 214 may be programmed with ablood glucose value corresponding to a threshold below which the user isconsidered to be hypoglycemic. As the processor module 214 receives asinputs multiple EGV's at time-spaced intervals, it processes the inputsby comparing each one to the programmed value, and also to previouslyreceived EGV's. If the user's blood glucose shows a downward trend, andis approaching the programmed value, the processor module 214 outputs analert such as the one shown in FIG. 4D to the smartphone's userinterface 222. The user thus receives an advance warning of a potentialhypoglycemic event, so that he or she can take appropriate action toavoid the hypoglycemic event.

In various other embodiments, the processor module 214 may change thecolor of the user interface 222 to reflect the user's current bloodglucose level. For example, the user's EGV may be displayed on thescreen as a number, as a trend graph, a horizontal bar graph, etc. Thetext and/or background on the user interface 222 may change when theuser's current blood glucose level transitions from one state toanother. For example, the text/background may show a first color, suchas green, if the user's blood glucose is within a healthy range, and asecond color, such as red, if the user's blood glucose is low or high.Alternatively, a first color may be used for the healthy range, a secondcolor for low, and a third color for high. Further, when in the low orhigh range, as the user's blood glucose becomes increasingly lower orhigher, the intensity of the color may increase. The text/background mayalso flash, with the frequency of the flashing increasing as the user'sblood glucose becomes increasingly lower or higher.

In these embodiments, the processor module 214 may be programmed withblood glucose values corresponding to low and high threshold BG values.As the processor module 214 receives as inputs multiple EGV's attime-spaced intervals, it processes the inputs by comparing each one tothe programmed values. If the user's blood glucose value crosses one ofthe thresholds, the processor module 214 outputs an alert to thesmartphone's user interface 222 in the form of changing the color of thetext and/or background. If the user's blood glucose value continues tobecome increasingly low or high, the processor module 214 producesadditional outputs, such as increasing the intensity of the color and/orcausing the text/background to flash. These additional outputs may begenerated in response to the processor module 214 comparing input EGV'sto additional programmed threshold values.

In various other embodiments, the processor module 214 may useiconography and/or alert symbols that reflect real time data. Forexample, the user's blood glucose becomes low, an icon on the smartphone18 may show an image of the user's BG trend graph using e.g., actualdata points from EGV's. The input-processing-output for this embodimentwould be substantially the same as that for the previous embodiment.

With extremely low blood glucose, a person can lose consciousness. Thus,in certain of the present embodiments, at a predetermined level or event(low glucose level, no button pressing after alert, etc.) that mightsignify a loss of consciousness, the processor module 214 may go into anemergency response instruction mode. This mode may include an alarm toalert others in close proximity to the user that something is wrong. Forexample, step-by-step instructions on how to assist the unconscious usermay be shown on the smartphone's user interface 222, such asadministering glucose tabs or another form of carbohydrates, calling anambulance, etc.

In these embodiments, the processor module 214 may receive an input froma CGM, which is the user's EGV. The processor module 214 may process theinput by comparing it to one or more threshold values, and determinethat the user's blood glucose is low. The processor module 214 mayproduce an output in the form of an alert. If the user does not respondto the alert by pressing a button or an area on a touchscreen userinterface 222, for example, the processor module 214 may determine thatthe user may be unconscious, and produces another output in the form ofthe emergency response instruction mode described herein.

In various other embodiments, the processor module 214 may providedifferentiated visual high/low thresholds versus alert thresholds. Forexample, the processor module 214 may be programmed with low and highblood glucose thresholds. These thresholds may be shown on a bloodglucose trend graph on the user interface 222 as horizontal lines thatthe user should strive not to cross. Ordinarily, crossing either of thelines might generate an alert. However, excessive alerts can be annoyingto the user, and can decrease patient compliance. Thus, in someembodiments, the visual high/low target range boundaries shown on thegraph may be different from boundaries that generate an alert. Forexample, the boundaries that generate an alert might be wider than thevisual target range threshold boundaries on the user interface 222, andthe boundaries that generate an alert may be hidden from view. Thisconfiguration gives the user a little bit of a buffer zone to crosseither of the visual boundaries without generating an alert.Alternatively, the boundaries that generate an alert might be visible,but distinguishable from the target range boundaries. Examples of visualdistinctions include different colors, flashing vs. static, solid vs.dashed, different line weights, an alarm icon adjacent the alarm lines,etc. In some embodiments, the high/low target boundaries may always bedisplayed, but the alert boundaries may be shown or not based on a usersetting, a mode (e.g. silent), thresholds, etc.

In various other embodiments, a user interface of the processor module214 may be the first thing that the user sees when he or she activatesthe smartphone's user interface 222. For example, many smartphones 18automatically put the user interface 222 to sleep (e.g., in a sleepmode) when no activity is detected for a predetermined amount of time.This measure saves battery power. To reactivate the user interface 222,the user must press a button on the smartphone 18. In certainembodiments, when the user reactivates the user interface 222, the firstthing he or she sees is the user interface of the processor module 214.In these embodiments, the processor module 214 receives as an input anotification that the user interface 222 has entered sleep mode,followed by a later notification that the user interface 222 has beenreactivated. The processor module 214 may process these inputs andproduce as an output a display of the user interface 222 of theprocessor module 214 on the smartphone 18.

In various other embodiments, a trend graph displayed by the processormodule 214 is color coded. For example, with reference to FIG. 4E, thecolor of the graph 400 (either the trend line 402 or the background 404)may be green if within a target range, yellow if ±10% outside the targetrange, orange if ±15% outside the target range, and red if ±20% outsidethe target range. A trend arrow 406 may be similarly color coded, andthe angle at which the trend arrow 406 is oriented may correspond to theactual rate of change of the user's glucose, e.g. a more horizontalarrow indicates a low rate of change, while a steeply sloping arrowindicates a high rate of change. In these embodiments, the processormodule 214 may receive as inputs continuous EGV's from a sensor system8. In some embodiments, the rate of change is calculated by the sensorsystem 8 and sent to the processor module 214 for display (e.g.,determination of how to display and resulting display), although theprocessor module 214 may also perform rate of change calculations. Therate of change based on a linear or non-linear function is applied to awindow of recent sensor data. In some embodiments, the rate of changecalculation comprises calculating at least two point-to-point rate ofchange calculations, and wherein the rate of change calculation furthercomprises adaptively selecting a filter to apply to the point-to-pointrate of change calculation based at least in part on a level of noisedetermined. The processor module 214 may output these values as datapoints on the trend graph 400 on the user interface 222 and also updatesthe value 408 shown in the box containing the user's most recent EGV. Ifthe user's blood glucose is dropping, the processor module 214 outputsthis information by orienting the arrow 406 downward, while if theuser's blood glucose is rising the processor module 214 outputs thisinformation by orienting the arrow 406 upward. In some embodiments, thetrend arrow is located on the end of the trend graph (e.g., rather an ina separate box/area).

In certain embodiments, a size of the value 408 shown in the boxcontaining the user's most recent EGV may change size depending on howfar off the user is from their target zone. For example, as the user'sglucose gets farther and farther away from the target zone, the numbercould get bigger and bigger. This amplification could be one directionalor either direction, meaning the EGV displayed on the trend graph couldget bigger and bigger if it's outside the target range in eitherdirection or only get bigger and bigger if it's outside the target rangeon the low side (e.g., hypo). The same applies to the trend arrow 406.With reference to FIG. 4F, the trend arrow 406 could be drawn largeenough to fit the EGV 408 inside the arrow 406. The layout of the trendarrow 406/EGV 408 in FIG. 4F may be used independently of the foregoingembodiment in which the size of the trend arrow 406/EGV 408 changesdynamically as the user's glucose changes.

In various other embodiments related to that of FIG. 4E, rather thanusing a hard threshold for the transition from one color to the next,the display could instead show a gradient type of trend graph. That is,rather than transitioning directly from green to yellow as soon as theuser's glucose hits the threshold of, say ±10% outside the target range,the display would instead gradually transition from green to yellow asthe user's glucose moves away from the target range toward theestablished threshold. Thus, at ±5% outside the target range, thedisplay would show a color between green and yellow, with the colorbecoming gradually more yellow as the user's glucose moves through ±6%outside the target range, ±7% outside the target range, ±8% outside thetarget range, etc.

Predictive Alerts/Alarms

In some embodiments, the sensor outputs a signal in the form ofelectrical current; however any output signal from any measurementtechnique may be used for the predictive alerts/alarms described herein.In general, a conversion function is applied to the sensor signal inorder to produce a user output that the user understands asrepresentative of a concentration of analyte in his or her bloodstream.Such a proper conversion function may take into account numerousvariables, e.g., sensitivity (slope), baseline (intercept), drift,temperature correction, factory-derived information or othercalibrations or adjustment to the data. After a suitable conversionfunction is applied, the user may see an output similar to that shown ine.g., FIGS. 3A to 3D.

In the scope of preventing the consequences of diabetes, it is desirableto prevent hypoglycemia and/or hyperglycemia episodes instead of simplygenerating alerts when such episodes occur. For example, an alertgenerated to indicate that without intervention a hypoglycemia episodewill occur within 20 minutes would allow the host or patient to ingestand absorb sugar in time.

Referring now to FIG. 5, an example of a continuous trace of glucosevalues measured during a time frame and having a threshold flag x₁ and apredictive flag x₂ located thereon is shown. As presented in FIG. 5, theexample glucose trace embodies a graph having a glucose level e.g.,mg/dl, compared against a time frame e.g., 24 hours.

As shown, there are three threshold values or limits used in themonitoring of the glucose values in some embodiments: TV₁, TV₂ andTV_(P). TV₁ may be settable by the user and generally defines the upperlimit or upper glucose value that a user may operate at before beingalerted by the monitor. Similarly, TV₂ may be settable by the user andgenerally defines the lower limit or lower glucose value that a user mayoperate at before being alerted by the monitor. TV_(P) is the predictivethreshold, e.g., the threshold against which a predicted value iscompared. It should be appreciated that although the illustratedembodiment envisions a threshold value, threshold ranges or othercriteria (e.g., glycemic states) may alternatively be used.

As shown, TV_(P) does not have a particular glucose value given. This isbecause TV_(P) may not be settable by the user; it may be a fixed valueor permanent value set during factory settings. In some embodiments,TV_(P) may represent a dangerously low glucose value e.g., be indicativeof a serious hypoglycemic event. In some embodiments, TV_(P) mayrepresent a value at or around 55 mg/dL.

In some embodiments, TV_(P) may be adaptively determined based on TV₂.For example, if the user sets TV₂ to be a relative high value, e.g., 90mg/dL, an algorithm or function may determine that TV_(P) should be setat 65 mg/dL. Conversely, if the user sets TV₂ to be a relative lowvalue, e.g., 70 mg/dL, an algorithm or function may determine thatTV_(P) should be set at 55 mg/dL. Additionally or alternatively, theprediction horizon, PH, may be preset or adaptively selected based onanother parameter associated with prediction, for example, based on TV₂selected and/or TV_(P).

Still referring to FIG. 5, there are shown two flags x₁ and x₂. Flag x₁may generally be considered a threshold flag, and may be configured toalert the user that a first threshold, e.g., TV₂, has been met. In someembodiments, threshold flag x₁ operates to notify the user in real time,or approximately real time (e.g., may take into account processingdelay, etc.) when a threshold has been met or crossed. An example ofusing threshold flag x₁ is when the user wants to be notified of whenhis glucose level hits a certain value, high or low. When the user'sglucose value is determined to meet that predetermined user set value,the user may be notified via an alert or alarm. Additional rate ofchange conditions may be added (e.g. TV₁ and rate of change increasingat >0.5 mg/dL/min or TV₂ and rate of change decreasing at >0.5mg/dL/min).

Flag x₂ may generally be considered a predictive flag, and may beconfigured to alert the user that a second threshold, e.g., TV_(P), ispredicted to be met within a predetermined or predefined time frame ortime horizon PH. In some embodiments, predictive flag x₂ operates tonotify the user in real time that a threshold is predicted to be crossedor met in a predefined time frame, e.g., 20 minutes.

An advantage of the glucose monitoring profile shown in FIG. 5 is thatprediction may be paired with a threshold alert that is settable by theuser, where an alert will sound for whichever condition is met first(threshold or prediction) Use of prediction algorithm parameters (e.g.,reliance on modeled versus measured data, time-sensitive weighting ofpast data, prediction horizon PH, and prediction threshold TV_(P)) tunedin advance may increase or optimize warning time for patients before asevere event occurs and may minimize the number of nuisance/false alarmsheard by user.

In some embodiments, the prediction parameters (TV_(P) and PH) may beinvisible to the user and preset or fixed. In some embodiments theprediction parameters are determined from the sensor manufacturer usinga variety of historical data using e.g., the user's historical data, apopulation historical data, the particular sensor's historical data,etc.

Referring back to FIG. 3A, an example glucose trace of a user's day isshown. From looking at the trace, it can be appreciated that the glucosetrace generally falls within a shaded or bound region 246. This regionmay be generally referred to as a “target zone”, and the user isencouraged to “stay between the lines”. This region is generallyviewable to a user on their monitor or device with a viewing screen ascan serve as a quick check on how a user's glucose looked for aparticular time period. Another example of the bound region is shown inFIG. 4A and designated as reference numeral 182.

In some embodiments, this shaded “target zone” differs from the regiondefined by TV₁ and TV₂, hereinafter referred to as the “alert boundary”.In some embodiments, TV₁ and TV₂ are not viewable by a user, but arerather internal values used by appropriate algorithms or functions toe.g., alert the user when necessary.

In other embodiments, the alert boundary may be viewable to the user. Itshould be appreciated that values, pictures or icons, or simpledesignations such as “HIGH” and “LOW” may be associated with TV₁ andTV₂, respectively. Additionally, TV_(P) may be shown, as a simple alarmicon, for example.

In some embodiments, the predictive alerts may be activated using asimple on/off button. Additionally, in some embodiments, there may beone or more general settings for prediction such as “sensitive”,“normal”, “non-annoying” for different user needs. For example, asensitive prediction setting may use a PH=30 minutes and TV_(P)=70mg/dL; a normal prediction setting may set values at PH=20 min andTV_(P)=55 mg/dL; and a non-annoying prediction setting may set values atPH=10 min and TV_(P)=55 mg/dL.

It should be noted that, although the disclosure focuses on predictionhorizon and criteria associated with prediction at low glucose(hypoglycemia), all of the principles applied to hypoglycemicalarms/alerts may be implemented for high glucose (hyperglycemia) as maybe appreciated by one skilled in the art.

FIG. 6 is a flowchart 500 illustrating a process for dynamically andintelligently providing a prediction alert/alarm in accordance with anembodiment of the disclosure. As explained above, providing a predictivealert/alarm is highly desirable as it may minimize and/or prevent thenumber of hypoglycemia and/or hyperglycemia episodes a user experiences.

At block 510, processor module 214 may be configured to receive sensordata (e.g., a data stream), including one or more time-spaced sensordata points, from the sensor 10. The sensor data point(s) can beaveraged, smoothed, and/or filtered in certain embodiments using afilter, for example, a finite impulse response (FIR) or infinite impulseresponse (IIR) filter.

At block 520, processor module 214 may be configured to evaluate thesensor data using a first set of instructions or criteria. The first setof instructions or criteria may include any algorithm suitable fordetermining if a data point has met one or more first predeterminedcriteria associated with hypoglycemia or hyperglycemia. Suchpredetermined criteria may be input by the user using e.g., a menu forinputting various alert thresholds. Alternatively, the predeterminedcriteria may be set according to factory settings and may be fixed, suchthat the user cannot change, e.g., the alert threshold. In someembodiments, the predetermined threshold is saved in e.g., a lookuptable, and may depend on other parameters such as time of day (e.g.,more or less sensitive at nighttime), historical patient information, orthe like. In other embodiments, more complex algorithms may be used todefine a user's current glycemic state, rather than static thresholds,such as static risk or dynamic risk models, where the criteria would bedefined based on the output of these complex algorithms or instructions(e.g., gradients, yes/no indicators, percentages, probabilities, or thelike).

In some embodiments, the sensor data comprises real time glucose values(e.g. blood glucose values (BGs)), calibrated glucose values (e.g.,estimated glucose values (EGVs)), rate of change of glucose values,direction of change of glucose values, acceleration or deceleration ofglucose values, insulin information, event information, historical trendanalysis results, and the like. Consequently, in some embodiments, theprocessor module 214 may be configured to evaluate sensor data using afirst function to determine whether a real time glucose value meets oneor more first predetermined criteria.

In some embodiments, the criteria may be thought of as including atleast one component. For example, the criteria may be representative ofa singular or absolute value. In some embodiments, the criteria maycomprise two or more components. For example, the threshold may berepresentative of a range of values. Alternatively, the threshold may berepresentative of a singular value associated with a time component. Inother embodiments, the threshold may be representative of a singularvalue associated with a direction or rate of direction.

As mentioned above, the one or more criteria may be a first thresholdvalue set by a user. For example, the user may decide that he wants tobe alerted whenever his glucose reading drops to 70 mg/dL or 70 mg/dLand rate of change is negative (indicative of decreasing glucose level).In other cases, the user may decide that 70 mg/dL might be to too low ofa reading, and wants to be alerted whenever his glucose drops below 80mg/dL. As understood by those of skill in the art, there is a finebalance between being alerted too often, e.g., nuisance alarms, andbeing adequately alerted when there is a real event. Consequently, theuser may be allowed to have some input on how often he is alerted, basedon selecting the first threshold value. The higher the selected value,the more sensitive the alert trigger, e.g., the more often a user isgoing to be alerted.

In some embodiments, the first threshold may be a user settable numberor a qualitatively sensitive indication for the threshold being crossed.The qualitatively sensitive indication may include settings such as“sensitive”, “normal”, “non-annoying”, etc., as described above. Forexample, the analyte monitoring system 8 may detect a high number ofalerts (e.g., >2 a day) and prompt the user with a question to determineif settings should be adjusted to avoid unnecessary annoyances. Forexample, system 8 may suggest to the user to change the qualitativelysensitive indication from sensitive to normal or from normal tonon-annoying if a higher than usual number of alerts (e.g., twice asusual) are being provided.

In some embodiments, the first set of instructions or criteria mayinclude any algorithm suitable for determining if a data point has metor crossed a predetermined threshold. In some embodiments the first setof instructions or criteria may include a more complex assessment ofglycemic state, including for example, other parameters such asamplitude and/or direction of rate of change of glucose, rate ofacceleration/deceleration of glucose, insulin and/or meal consumption.In some embodiments, the first set of instructions or criteria may use ameasure of risk, such as the static risk and/or dynamic risk models forcontinuous glucose monitoring data to generate and determine whether aparticular criterion has been met. In some embodiments, the algorithmperforms its calculations on uncalibrated sensor data, after which theresults are converted into calibrated data and compared to the criteriaand/or threshold. Performing some or all of the algorithmic processingon uncalibrated data may be advantageous to reduce negative impacts oferror or bias associated with calibration; however in some embodiments,the algorithmic processing may be performed on calibrated data.

In other embodiments, more complex algorithms may be used to define auser's predicted glycemic state, rather than static thresholds, such asstatic risk or dynamic risk models, where the input could includereal-time or predicted values and where the criteria would be definedbased on the output of these complex algorithms (gradients, yes/noindicators, percentages, probabilities, or the like).

At block 530, processor module 214 may be configured to evaluate thesensor data using a second set of instructions or criteria. In someembodiments, the first set of instructions from block 520 is differentthan the second set of instructions from block 530.

Similar to block 520, in block 530, the second set of instructions orcriteria may include any algorithm or algorithms suitable for evaluatingsensor data to determine whether a glycemic state (hyper- orhypo-gylcemia) is predicted, for example, by determining if a data pointis predicted or projected to cross a predetermined threshold. Suitablealgorithms include algorithms based on polynomial and auto-regressivemodels, algorithms based on Kalman Filtering (KF), artificial neuralnetworks, statistical and numerical logical algorithms, and machinelearning.

In some embodiments, the second set of instructions may use utilize anartificial neural network that can consider other relevant information,if available, such as food, exercise, stress, illness or surgery topredict a future glucose value. The architecture may include threelayers, with a first layer gathering the inputs, a hidden layer thattransforms the inputs using polynomial or non-linear functions, e.g.squaring, sigmoidal, thresholding, and a third output layer thatcombines the outputs of hidden layer into output or predicted value(s).Neurons may be totally connected and feedforward. One useful artificialneural network implementation is described by W. A Sandham, D.Nikoletou. D. J. Hamilton, K. Patterson. A Japp and C. MacGregor, in“Blood glucose prediction for diabetes therapy using a recurrentartificial neural network,” in IX European Signal Processing Conference(EUSIPCO), Rhodes, 1998, pp. 673-676. Each neuron in second and thirdlayers takes as input a weight combination of outputs from the previouslayer. These weights are tuned to give the best predicted value througha process called training. That is, a neural network starts with aninitial guess and using training and testing data sets with known inputand outputs, finds the best possible weights to give optimal outputs.Once the training process is complete, the network may be used topredict outputs for any new data. The network input information may bethe current glucose measurement and its timestamps together with alimited number of previous glucose samples from the CGM system. The NNM(neural network model) may take into account the glucose measurements upto 20 minutes before the current time. Since the sampling rate variesfrom one CGM system to another, the number of the NNM inputs may bedifferent for each dataset. The output of the network may be the glucoseprediction at the prediction horizon time.

In some embodiments, the second set of instructions may use anautoregressive model (first order, second order or third order, forexample) to predict a future glucose value. One useful first-orderautoregressive model implementation is described by G. Sparacino, F.Zanderigo. S. Corazza, A. Maran, A. Facchinetti and C. Cobelli, in“Glucose Concentration can be Predicted. Ahead in Time From ContinuousGlucose Monitoring Sensor Time-Series.” Biomedical Engineering. IEEETransactions 011. 2007, voL 54, pp. 931-937. This algorithm predicts thefuture glucose value by taking the current glucose value y(n) andmultiplying the previous glucose value y(n−1) by some coefficient α.When glucose is rising, a will be some value slightly larger than 1 andwhen it is falling a will be a little less than 1. In this algorithm,the model parameter (a) may be estimated recursively (e.g., updatedevery 5 minutes to account for glucose dynamics) to minimize the sum ofsquared residuals for all pairs of predicted and current glucose values.An estimate of alpha may be updated (e.g., using a weighted leastsquares regression) every time a new sensor data point is received(e.g., every 5 minutes). Prediction parameters (e.g., forgetting factor,prediction horizon, and prediction threshold) may be tuned in advance tooptimize warning time for patients before a severe hypo event occurs(e.g., 55 mg/dL), and to minimize the number of nuisance/false alertsheard by patient. For example, the direction of glucose changes overtime, so a forgetting factor μ may be used to weight the most recentdata more heavily with a value between 0 and 1. Prediction horizonand/or prediction thresholds may be predetermined by the system and/oruser selectable as described in more detail elsewhere herein.

In some embodiments, the first order autoregressive model includes aforgetting factor, a prediction horizon and a prediction threshold tunedto provide no more than one additional alarm per week based on aretrospective analysis comparing the use of the first function and thesecond function together as compared to the first function alone. Insome embodiments, this is at least partially achieved by monitoring theuser's qualitatively sensitive indication and prompting or urging theuser to select the appropriate setting.

In some embodiments, the second set of instructions may use a Kalmanfilter (an optimal estimation method) to predict a future glucose value.The Kalman filter trades off the probability that a measured glucosechange is due to sensor noise versus an actual change in glucose, toobtain the maximum likelihood estimate of glucose (and its first andsecond derivatives). One useful Kalman Filter implementation isdescribed by Palerm, C. and Bequette, W. in “Hypoglycemia Detection andPrediction Using Continuous Glucose Monitoring—A Study on HypoglycemicClamp Data,” J Diabetes Sci Technol. 2007 September; 1(5): 624-629. Thestates are the blood glucose concentration (g_(k)), its rate of change(d_(k), e.g., the velocity), and the rate of change of the rate ofchange (f_(k), e.g., the acceleration). The latter is assumed to vary ina random fashion, driven by input noise w_(k) (with covariance matrixQ), which describes changes to the process. The sensor glucosemeasurements are assumed to contain noise, described by υ_(k) (withcovariance matrix R). Prediction model parameters (e.g., Q/R ratio,prediction horizon, and prediction threshold) may be tuned in advanceand/or user selectable.

In some embodiments, the second set of instructions or function mayoptionally include a mechanism to include user input, such as insulin,exercise, meals, stress, illness, historical patient information (e.g.,patterns or trends), etc.

In some embodiments, the sensor data comprises real time glucose values,(e.g. blood glucose values (BGs)), calibrated glucose values (e.g.,estimated glucose values (EGVs)), rate of change of glucose values,direction of change of glucose values, acceleration or deceleration ofglucose values, insulin information, event information, historical trendanalysis results, and the like Consequently, in some embodiments, theprocessor module 214 may be configured to evaluate sensor data using asecond function to determine whether a predicted glucose value meets oneor more predictive alarm criteria.

The second predetermined criteria may include a single value, a range ofvalues, a direction associated with the value, a rate of direction, etc.In some embodiments, the second criteria include a predeterminedthreshold, which is a fixed value set as part of factory settings. Forexample, it may be desirable to have a second threshold that the usercannot manipulate or change because of the importance associated withthe threshold. For example, in some embodiments, the secondpredetermined threshold may represent a value that is indicative of asevere hypoglycemic event, e.g., 55 mg/dL.

In some embodiments, the second predetermined criteria is determinedbased on a probability of hypoglycemia within a particular time frame,which may account for the rate at which sensor data is changing, thedirection the sensor data is going, the current glucose level, pasthistory of glucose change, insulin information, meal information,exercise information, etc. An example of applying additional criteria tothe processor module 214 is that if hypoglycemia is predicted in thenear future but the current glucose level is 200 mg/dL then thatprediction is not probable or likely, thus, limits on glucose value,rates of changes, and the like, may be applied. Another example, thistime of using meal information, is that if the user indicated that theyrecently ate a meal, then it may be the glucose will quickly turn aroundand it is not necessary to alert them at this time.

In some embodiments, the second predetermined criteria may be at leastpartially or adaptively based on the first predetermined threshold. Forexample, a suitable set of algorithms or functions may base e.g., theprediction horizon and/or the second predetermined threshold on thefirst predetermined threshold.

In some embodiments, the second set of instructions is completelypredictive, meaning that the instructions use past and/or current datato determine if a user will meet or cross through the secondpredetermined threshold in a predetermined time frame or horizon. Thepredetermined time frame or horizon is preferably long enough for a userto take action to turn around a predicted event. For example, thepredetermined horizon may be at least 15 minutes in some embodiments, atleast 20 minutes in some embodiments, and at least 30 minutes in someembodiments.

In some embodiments, the predetermined time frame or horizon may haveaddition capabilities or take additional information into considerationwhen setting the time frame. For example, as is known by those of skillin the art, some continuous glucose monitoring systems sense glucose inthe interstitial fluid, rather than blood (e.g., capillary blood fromwhich finger stick measurements are traditionally derived).Consequently, there may be a time lag between the value measured and theactual blood glucose value, e.g. 5 minutes or more. In some examples thetime lag can be as small as 0 minutes, where in other examples the timelag is as large as 15 minutes or more. There may also be variability inthe time lag, which may be represented by a standard deviation in themeasurement, e.g., 10 minutes for a 5 minute lag.

As sensors become more accurate through improved sensor design, theinaccuracy due to time lag may become a more substantial contributor tothe total error. While not wishing to be bound by any particular theory,it is suspected that physiology, time since the sensor was inserted,glucose state (hypo- or hyper-), glucose rate of change, knowledge offiltering performed, and other variables may influence the amount oftime lag a sensor will experience. Consequently, in some embodiments, atime lag adjustment algorithm or set of instructions may be used indetermining or in conjunction with setting the predetermined time frameor time horizon. In some embodiments, the time lag adjustment set ofinstructions uses near time prediction to predict and display theestimated glucose value at a future point in time, e.g. from about 2.5minutes to about 15 minutes, depending on additional information e.g.,obtained through algorithmic or alternate detection means. For example,information that may influence the prediction horizon include: timesince the sensor was inserted, glucose state or rate of change, adaptivelearning algorithms that can learn what each individual's average timelag is and apply a unique setting, etc. Consequently, in someembodiments, a time adjustment lag algorithm may use as one or moreinputs, one or more of the following variables: time since the sensorwas inserted, glucose state or rate of change, user a prioriinformation. In some embodiments, additional sensors may be implementedto directly or indirectly measure time lag. An example sensor could bethe use of impedance data between the working and reference electrodesthat are separated in space or between an electrode at the sensing areaand a secondary electrode on the surface of the skin.

In some embodiments, blocks 520 and/or 530 may be iteratively repeatedwith the receipt of any new data, including data derived from thesensor, another medical device (e.g., insulin delivery device) and/orthe user or host (via user input) prior to the activation of ahypoglycemic indicator.

At block 540, processor module 214 may be configured to activate ahypoglycemic indicator if either the first criteria is met or if thesecond criteria is predicted to be met. In some embodiments, theprocessor module 214 is configured to determine which set ofinstructions has met its criteria (e.g., 520 or 530 where x₁ or x₂ isflagged). For example, the first set of instructions may determine firstthreshold has been met at 8:47 pm in block 520, using real time data. Inthe same example, the second set of instructions may determine secondthreshold is predicted to be met at 9:00 pm in block 530, using the 20minute prediction horizon. In this example then, where both the firstand second evaluations have met their criteria, either or both may beused in subsequent processing.

As described above, the hypoglycemic indicator may comprise a singleindicator representative of whichever function determines or predictsthat a threshold will be crossed first in time. In some embodiments, thehypoglycemic indicator comprises a flag that has a particular set ofinstructions associated with it, depending on whether the firstevaluation using the first criteria (x₁) or the second evaluation usingthe second criteria (x₂) were met. The responses to the first or secondevaluations may also be differentiated in processing and/or outputdifferently. In some embodiments, where both evaluations show criteriabeing met, there is unique processing and output indicating both actualand prediction-based criteria were met. Alternatively, rules may beprovided to determine which criteria were met.

For example, in some embodiments, the x₁ and x₂ flags may bedifferentiated on the user display or user interface 222. In someembodiments, the user interface 222 may be configured to show thesimultaneous display or both criteria (x₁ and x₂), such as “x₁ is at 76mg/dL” and “x₂ is predicted to be at 55 mg/dL in 20 minutes.”

At block 550, processor module 214 may be configured to provide an alertor alarm if warranted by the detection of a threshold being met orpredicted to be met, as represented by the hypoglycemic indicator. Insome embodiments, the processor module 214 provides an alarm or alertbased on the detection of the threshold that is met or predicted to bemet at an earliest time point. In some embodiments, providing an alertbased on the earliest detected or predicted threshold is desirablebecause it may provide a user more time to turn around an actual orpredicted a hypoglycemic and/or hyperglycemic event.

In addition to sending any of the above-described sensor data to aninsulin delivery device, in some embodiments, the processor module 214may send a message to an insulin delivery device including at least oneof: a) suspend insulin delivery (e.g. configured to suspend a basal orbolus delivery of glucose in an insulin delivery device responsive tosensor data meeting a predetermined criterion), b) initiate ahypoglycemia and/or hyperglycemia minimizer algorithm (e.g. an algorithmconfigured to control a user's blood glucose to a target range using anautomatic insulin delivery device), c) control insulin deliveryresponsive thereto or d) information associated with the alert (e.g.,hypoglycemia indicator). Sensor data and/or messages may be sentdirectly to a dedicated insulin delivery device or indirectly through acontroller, such as in a smart phone or via the cloud. Some embodimentsprovide the necessary sensor data useful for a hypoglycemichypo-avoidance system to be sent to the insulin delivery device, forexample, a hypoglycemic indicator may include information useful fordetermining when and for how long to suspend or reduce basal insulindelivery (e.g., predicted glucose value and prediction horizon, rate ofchange of glucose, static risk, dynamic risk) or may send the actualinstructions (e.g., suspend basal delivery for 20 minutes).

In some embodiments, the alert and/or alert settings may be manipulatedby a user or set one or more thresholds (e.g., from about 60-100 mg/dL).In some embodiments, the user can disable/enable the first and/or secondfunctions. In some embodiments, a single message indicating an “earlywarning” or alert may be provided to the user. In some embodiments,particularly in the context of a closed loop or semi-closed loopsystems, the settings are configured based on a control algorithm, whichmay be programmable or downloadable by a user.

In some embodiments, the alert may be selected from the group including:audible, tactile, visual and/or data transmissions (e.g., to a remotemonitoring site) to subsequent audible, tactile and/or visual output toa user interface 222. For example, if a child host's alarm is activated,the data may be transmitted to a parent's smartphone (and the same ordifferent alarm information may be provided). In such an example, thechild host may receive an audible alarm, while the parent may receive adetailed glucose log. In some embodiments, if the first functiondetermines that the first threshold is met first, an alert is providedto a user. In other embodiments, if the second function predicts thatthe second threshold will be met first, an alarm is provided to a user.

In some embodiments, a prediction alarm could trump a threshold alert togive a greater sense of urgency. For example, if the prediction alarm isassociated with a reading of 55 mg/dL having a 20 minute predictionhorizon, and the threshold alert is associated with a reading of 70mg/dL, the prediction alarm indicator would be configured to control thealert screens and output.

In some embodiments, prediction alarm sounds/screens may convey agreater sense of urgency than the settable threshold alert. A rationalefor having a differing sound/screen for alarms and alerts is to assistthe user in understanding the difference between the two thresholds,e.g., that one may be a routine alert, while the other is indicative ofa serious event. In some embodiments, whichever alert is met orpredicted to be met first in time, is the alert conveyed to the user. Itshould be appreciated that these predictive and threshold alerts aredifferent than target alerts, which may be used to “keep a user betweenthe lines”, such as described above with respect to FIGS. 3B and 4A. Insome embodiments, information from the results of both the first andsecond sets of instructions may be combined and relayed on a userinterface, whether or not they both met certain criteria. For example, acombined alert screen would include both a predictive alarm (e.g., anindication that the host is below the alert threshold (TV₂) and ispredicted to be below predicted threshold TV_(P) within 20 minutes or anindication that the host is below the alert threshold (TV₂) but is notpredicted to be below predicted threshold TV_(P) within 20 minutes). Insome embodiments, additional information, such as therapyrecommendations or requests for additional information may be displayed.

Once a hypoglycemic alert or alarm has been triggered, the processormodule 214 may continue onto the post-alert monitoring, described inmore detail below. Additionally or alternatively, where a systemincludes remote monitoring (e.g., a remote electronic device receivingand tracking a user, such as a parent's cell phone or a caretaker'spersonal computer), once a hypoglycemic alert has been triggered, andsent to the remote device, an increase in communication, datatransmission, or the like, may be initiated to allow the caretaker orparent to more closely monitor the patient during hypoglycemic orpredicted hypoglycemic events. The increase in communication or datatransmission can include more frequent pushing and pulling of sensordata, additional input from or messaging to or from the remote device,or the like. The request for additional information may originate at theremote device and processor module 214 may be configured to receive andprocess such requests for additional information directly from theremote device.

Post-Alert Monitoring

Another issue commonly confronted with continuous glucose monitors, isthat once an alert or alarm is triggered, the user may remain at orabout the threshold value that triggered the alert, and may thereaftercontinue to trigger repeat or redundant alerts. Such alerts may be anuisance to the user, causing them to suspend (e.g., snooze) alert/alarmfeature of the monitor. A snooze that considers only time (e.g., ratherthan additional sensor data) may be undesirable, as there may besituations following the initial trigger of an alarm that would requirean additional alarm to be provided to the user. If the user hassuspended the alert/alarm feature, and suspend or “snooze” feature issolely time-based, the user may not be aware that they are in danger ofapproaching a hypoglycemic and/or hyperglycemic event during suspend orsnooze time period.

Referring now to FIG. 7, an example of a continuous trace of glucosevalues measured during a time frame showing different scenarios that maycause an alert/alarm to be repeated is shown. Similar to FIG. 5, in FIG.7, the example continuous glucose trace may embody a graph having aglucose level e.g., mg/dL, compared against a time frame e.g., 24 hours.

As shown, there are three threshold values or limits used in themonitoring of the glucose values: TV_(I), TV₂ and TV_(P). These valueshave already been discussed in the FIG. 5 discussion above.

In FIG. 7, there is a region or zone defined between a first and secondtime point TA_(1S) and TA_(1E). Time point TA_(1S) may generally bereferred to as a time that the first alert begins or starts. Assumingthe user has acknowledged the alert soon after Time point TA_(1S), Timepoint TA_(1E) may generally be referred to as the time that the user'sglycemic state changes. As shown, at TA_(1S), a user may first beprovided an alert that a threshold value TV₂ has been met. In the timeinterval following TA_(1S), the user's glucose value generally hovers oroscillates around the same glucose level that triggered the initialalert, during which time re-alerts should not occur, and after whichre-alerts should occur.

In some conventional systems, each time a user's glucose value wouldcross a threshold such as TV₁ and TV₂, the user would be alerted againthat a threshold had been crossed. This would be annoying at best, andmay even influence a user to turn off the alarms of the sensor followingthe first alert TA_(1S). Further, if the user turned off or even“snoozed” or the one or more threshold alerts (e.g., at 80 mg/dl) usinga simple time-based snooze, the user may not be aware of the worseningcondition after TA_(1E).

Still referring to FIG. 7, the region or zone where the glucose valueoscillates may be further defined by a first and/or second glucosethreshold TV_(Z1) and TV_(Z2). First glucose threshold value TV_(Z1) maygenerally be referred to as the upper region or zone limit for a valuethat oscillates around e.g., TV₂. Second glucose threshold value TV_(Z2)may generally be referred to as the lower region or zone limit for avalue that oscillates around e.g., TV₂. In some embodiments, takentogether, TV_(Z1) and TV_(Z2) provide a buffer zone, which also may bereferred to as a margin (e.g., +/−5, 10, 15% or +/−5, 10, 15, 20 mg/dL)above and below a threshold value that would trigger an initial alert bywhich few, if any, repeat alerts are provided to the user. In otherwords, after the initial alert is provided to the user at TA_(1S) andthe user acknowledges the alert, the user may not be re-alerted untilafter TA_(1E), e.g., when the glucose value exits the buffer zone (e.g.,change in glycemic state). In some embodiments, TV_(Z1) and TV_(Z2) mayhave asymmetrical bounds and/or no lower bound (no TV_(Z2)). For atleast the reason that the glucose trace scenario suggests, moreintelligent and dynamic decision making may be advantageous such that auser is not overly alarmed in certain situations (e.g., between TA_(1S)and TA_(1E)), but sufficiently alarmed in other situations (e.g., afterTA_(1E)). Such a buffer zone, also referred to as margins or deltasherein, may be bidirectional and/or asymmetrical depending on thecondition.

FIG. 8 is a flowchart illustrating a process 700 for dynamically andintelligently monitoring the hosts' glycemic condition after activatingan alert state based on the sensor data meeting one or more activetransition criteria associated with a hypoglycemic condition orhyperglycemic condition, for example, as described with reference toblock 540. Additionally, post alert monitoring may be applied for anyalarm or alert, whether evaluating against multiple criteria as inblocks 520 and 530, or simply compared against a single threshold (e.g.,hypoglycemic or hyperglycemic threshold). In some embodiments, thetriggering of an alert or alarm is merely the flagging or marking of thealert or alarm, for example, some hyperglycemic alert conditions includea wait time (e.g., 0 to 120 minutes), for example, an “enable waitbefore first alert” option that the user can set/enable. When this inputenabled, the wait time will be applied before alerting the user for thefirst time, as described in more detail elsewhere herein, unless theactively monitoring determines a worsening condition.

In some embodiments, the triggering of an alert or alarm includesproviding the alert or alarm to the user. In general, the processormodule may be configured to provide an output associated with the activealert state, wherein the output is indicative of the hypoglycemiccondition or hyperglycemic condition. In some embodiments, thepost-alert monitoring will begin regardless of whether a user hasacknowledged the alert, where the acknowledgement will alter thepost-alert state as described elsewhere herein. In some embodiments,post-alert monitoring begins with a transition from the active state tothe acknowledged state.

Criteria for triggering an alert, also referred to as transitioning froman inactive state to active state or activation criteria, may be anycriteria or threshold associated with hypoglycemia or hyperglycemia.Activation criteria may include glucose value, predicted glucose value,rate of change of glucose (direction and/or amplitude), rate ofacceleration of glucose (direction and/or amplitude), static riskmodels, dynamic risk models, or the like. Additionally or alternatively,activation criteria disclosed for transitioning 1055 from inactive state1030 to active state 1010 may initiate the dynamic and intelligentmonitoring the hosts' glycemic condition after activating an alert stateassociated with hypoglycemia or hyperglycemia.

At block 710, processor module 214 may be configured to actively monitordata associated with the host's hypoglycemic or hyperglycemic conditionfor a time period following the triggering or an alert/alarm to the user(e.g., as described in FIG. 6 and associated text) and/or useracknowledgement. In some embodiments, the user or host has acknowledgedthe initial alert. As described in more detail elsewhere herein,acknowledgement of an alarm may include a user interaction with thesystem (“user action”), such as a button or menu selection, or otherdata input (e.g., meal or insulin information entered). Additionally oralternatively, acknowledgement of an alarm may be based on monitoring ofdata associated with the glycemic state, including sensor data, insulindata, meal data, or the like. In some embodiments, the acknowledgementmay come from a remote monitor, such as a parent's cell phone, or thelike. Acknowledgement of an alarm may cause the processor module 214 totransition from the active state to the acknowledged state, during whichactive monitoring may occur.

In some embodiments, processor module 214 actively monitors dataassociated with the host's hypoglycemic or hyperglycemic condition for atime period in the acknowledged state or continuously while in theacknowledged state until a state transition to inactive (e.g. based oninactivation criteria). In some embodiments, processor module 214monitors sensor data or other data received post-alert activation. Forexample, the data may include information such as: sensor data (e.g.,glucose levels, trends, distances between peaks and valleys indicativeof meal timing, etc), sensor diagnostic information (noise indicators),meal information (e.g., caloric intake and time of intake), insulininformation, or other event information. In some embodiments, activelymonitoring data includes determining an average glucose over a window oftime, an amplitude and/or direction of rate of change of glucose or anamplitude and/or direction of rate of acceleration of glucose.

In some embodiments, processor module 214 tracks how quickly and/or howoften the user acknowledges the alarms to determine further processing.For example, patterns that evaluate timing of user acknowledgement,information about the type of alert that was triggered and/or resultingrecovery from the condition may be evaluated and future acknowledgementsor alerts modified based thereon. In general, however, the processormodule 214 may process alarms based on the assumption that a useracknowledgement of the alert/alarm indicates the user is aware of theconditions. Accordingly, re-alert conditions may be different, e.g.,more stringent, in some embodiments, as described herein.

In some embodiments, the sensor data is actively monitored during theacknowledgement time period, also referred to as the active monitoringtime period. Suitable time periods include, for example, 20 minute, 40minute, 60 minute, etc., time periods, and may be user settable. Suchtime periods may commence or begin at the first data point following thealert or alarm triggering data point. In some embodiments, the user maybe allowed to select the time period. In some embodiments, thepost-alert sensor data monitoring continues until the alert isinactivated, as described in more detail elsewhere herein.

At block 720, processor module 214 is configured to transition from theacknowledged state to at least one of an inactive state or active stateresponsive to the data associated with the host's hypoglycemic orhyperglycemic condition meeting one or more predetermined criteria. Theprocessor module 214 may determine if there has been a change in statebased on the sensor data or other data associated with the host'sglycemic condition. In some embodiments, the change in state may beindicative of a change in the host's glycemic condition.

In some embodiments, a change in state may be a positive event, such asan indication that a user has safely turned around a predictedhypoglycemic event, which may trigger the acknowledged state torecognize a sub-state of recovery and/or inactive state, as described inmore detail elsewhere herein. In other embodiments, a change in statusmay be a negative event, such as an indication that a user has droppedfurther toward a hypoglycemic event (e.g., acceleration/decelerationanalysis of the sensor data) for a particular time period (e.g., 20minutes), with or without a sub-state of recovery being determined. Inthis second example discussing the negative event, it may be desirableto reactivate the alert, even when a user has acknowledged the alert.This second alert or alarm may be valuable in warning the user that hiscondition is declining—that whatever action he took following the firstalarm, if any, was insufficient to turn around or improve his glucosereading. In some embodiments, a status change indicative of such anegative event may override an acknowledged state (described elsewhereherein), causing the system to transition back into an active alertstate (also referred to as re-alert or reactivation). However, if thesystem was already in an active alert state (e.g., unacknowledged by auser), the resulting output may escalate, e.g., louder and morefrequent, and/or may result in a communication with 911, a care taker,or the like.

In some embodiments, processor module 214 may be configured to determineno change has occurred over the last x minutes, for example 15, 30, 45or 60 minutes, such as described with reference to conditions associatedwith remaining in a particular state (e.g., active state) and/orreactivation conditions as described in more detail elsewhere herein. Nochange may occur, for example, in situations where the data points arehovering around the threshold value that triggered the initial alert(e.g., with low rates of change). It may be appreciated that in suchsituations, each time a data point crosses the threshold in anundesirable direction, e.g., below a threshold for a hypoglycemic eventor above a threshold for a hyperglycemic event, an alert or alarm wouldbe triggered in conventional systems. This is typically thought of as anuisance, and may be a reason a user would act to turn off his alerts orbecome desensitized to the alerts. In these situations, the intelligentpost-alert monitoring algorithms would likely avoid these annoyingalerts (e.g., as long as a user remains in the same state and/orreactivation conditions have not been met, which are described in moredetail elsewhere herein).

As used herein, the user may be in different monitoring “states” suchthat processor module 214 can detect when the user transitions from onestate to another. Examples of such states include, for example,“active”, “inactive” and “acknowledged”.

The active state is herein defined as an alert state post-alert trigger,where data is being monitored to determine whether to change the alertstate to “acknowledged” or “inactive.” The active state may be enteredfrom an inactive and/or acknowledged state by comparing data (sensor,glucose, insulin, user provided information, etc.) against differentcriteria, respectively for each state transition. Re-alerts and/orreactivation of the alerts may occur in this state, which escalate theinitial alert, even when an acknowledgement has not occurred.

The inactive state is herein defined as an alert state post-alerttrigger, where evaluation of data indicates the glycemic state is in asafe or target zone. The inactive state may be entered from an activeand/or acknowledged state by comparing data (sensor, insulin, userprovided information, etc.) against different criteria, respectively foreach state transition.

The acknowledged state is herein defined as an alert state post-alerttrigger, where the user has acknowledged an alert and/or alarm and whereno additional alerts/alarms will be provided for a predetermined timeperiod, unless certain re-activation criteria are met (wherein thereactivation criteria are different and/or more stringent that theinitial activation criteria, for example, which is described in moredetail elsewhere herein). The acknowledged state may be entered from theactive state based on user interaction and/or data indicative of arecovery from the hyper- or hypoglycemic condition. It should beappreciated that the terms used herein for the states and/or functionsare merely descriptive and may go by other names, provided the functionsremain substantially the same.

In some embodiments, a user may acknowledge an alert by pressing abutton, selecting a menu screen, or the like. In some embodiments, atimer is set for a predetermined time period when the system enters theacknowledged state, after which predetermined time period the state mayautomatically transition into active and/or inactive based on selectedconditions.

In some embodiments, the acknowledged state evaluates data associatedwith the hosts' glycemic condition (including glucose, meal and/orinsulin information) and may transition into the acknowledged statebased on the evaluation indicating the host's recovery from the glycemiccondition that triggered the alert. The host's recovery may be alsoconsidered as a sub-state of acknowledged and may be determined bycomparing data (sensor, glucose, insulin, user provided information,etc.) against different criteria, respectively for each statetransition. Re-alerts may be suspended for some time period, when in theacknowledged state and/or acknowledged and recovering sub-state.

In some embodiments, if the user does not acknowledge the alert, thealert remains in active state. In some embodiments, if the user does notacknowledge the alert after X minutes (e.g., 5, 10, 15 minutes), thealert may be escalated via output to a secondary display device, aremote monitor, emergency contact, etc. Similarly, if the conditionsworsens during active monitoring (e.g., in acknowledged state), thealert may be escalated via output to a secondary display device, remotemonitor, emergency contact, etc.

An example showing the various transitions from one state to another isprovided in the discussion of FIG. 11 below. It should be appreciatedthat using different states to describe that user or host's glycemiccondition is useful in tracking the user's condition after an alert oralarm has been triggered. Thus, certain trends, such as a user'sglycemic condition improving or just hovering about a non-serious alertmay be reason enough to discontinue alerting the user. However, othertrends, such as a user's rapid decline in glycemic condition or evenhovering about a serious alert, may be reason to continue to warn oralarm the user. A number of suitable responses (e.g., associated withreactivation of an active alert) may be saved in an e.g., lookup table,depending on the state transition and other known information.

Referring now to FIG. 11, a state diagram 1000 is provided that showsthe transitions between the following states: active (A) 1010,acknowledged (K) 1020, and inactive (I) 1030. These states and theirtransitions may be described as follows:

Upon activation of an alert, the processor module transitions (1055)from an inactive state (1030) to an active state (1010). Conditions foractivation include various criteria or thresholds, hereinafter referredto as “activation criteria” or “activation conditions”, useful fordetecting actual or upcoming hyperglycemia or hypoglycemia, such asdescribed in more detail elsewhere herein (e.g., FIG. 6). Alertconditions are met as designated by reference numeral 1055. In additionto sensor data (glucose values, derivatives (rate of change) and doublederivatives thereof (acceleration)), alert conditions may relate toother data including insulin data and/or user input (meal information,exercise information, etc.). Additionally, embodiments discussed hereinwith respect to reactivation may be applied here (e.g., analysis ofstatic risk and/or dynamic risk). However, regardless of the detectionmethod for an active hypoglycemic or hyperglycemic condition, thedisclosed active monitoring and state transitions may apply.

In some embodiments, certain of the following activation conditions mayapply for a state transition from inactive to active based on ahyperglycemic condition: the glucose level exceeds a predeterminedthreshold (e.g., 160, 180, 200, 220 mg/dL); the average glucose levelover a predetermined time period (e.g., 10, 20, 30, 40 minutes) is morethan a predetermined threshold (e.g., 160, 180, 200, 220 mg/dL); thecurrent glucose level is more than a predetermined threshold plus apredetermined margin (e.g., 25, 50, 75 mg/dL); and/or the rate of changeof glucose is greater than a predetermined threshold (e.g., −0.5 mg/dL).

In some embodiments, the following activation conditions may apply for astate transition from inactive to active based on a hypoglycemiccondition: glucose level is less than a predetermined threshold (e.g.,80, 60, 60 mg/dL); glucose level is less than a predetermined thresholdand rate of change is less than is a predetermined rate (1.0 mg/dL/min,e.g., not rising rapidly); or glucose level is predicted to go below asecond threshold (e.g., 55 mg/dL) within a prediction horizon (e.g., 10,15, 20 minutes).

In some embodiments, transitioning (1080) from the active state (1010)to an acknowledged state (1020) is based on data or acknowledgementcriteria indicative of the host's glucose trending toward euglycemia,wherein the data may include sensor data indicative of a change inglucose trend and/or insulin information associated with a correction ofthe condition.

For example, upon activation of an alert indicative of hyper- orhypoglycemia, sensor data indicative of a change in glucose trend mayinclude evaluating sensor data whereby a direction and/or amplitude ofglucose level, rate of change of glucose level oracceleration/deceleration of glucose level indicative of a trend backtoward euglycemia (e.g., change in direction or trend) is sufficient toautomatically transition from active to acknowledged state (with orwithout user interaction with a user interface). A trend back towardeuglycemia may also flag or trigger a “recovering” sub-state, which is astatus within the acknowledged state indicative of trend back towardeuglycemia. In one example, after an active transition associated with ahyperglycemic condition, a state transition to acknowledged, butrecovering sub-state, may be based on an acknowledgement criteria orcondition such as the glucose level (or average glucose level over apredetermined time period) is less than a predetermined threshold minusa delta (e.g., 10, 15, 20 mg/dL), and in some examples with a conditionon rate of change trending toward euglycemia (e.g., decreasing fasterthan about 1 mg/dL/min). Similarly, after an active transitionassociated with a hypoglycemic condition, a state transition toacknowledged, but recovering sub-state, may be based on anacknowledgement criteria or condition such as the glucose level (oraverage glucose level over a predetermined time period) is greater thana predetermined threshold plus a delta (e.g., 10, 15, 20 mg/dL), and insome examples with a condition on rate of change trending towardeuglycemia (e.g., increasing faster than about 1 mg/dL/min).

In some embodiments, transitioning (1075) from the active state (1010)to an acknowledged state (1020) is based on acknowledgement criteriaindicative of a user acknowledgement of the alert on a user interface,user input insulin information and/or user input of meal information.For example, the user may hit a touch screen “button” to acknowledge thealert. Additionally or alternatively, when the continuous glucose sensoris operably connected to an insulin delivery device (including a remoteprogrammer associated therewith), changes associated with basal or bolusdelivery profiles or amounts may be considered as user input,particularly when the change was initiated by user interaction.Similarly, when the continuous glucose sensor is operably connected toan electronic device (or integral with the electronic device) capable ofreceiving meal information (e.g., carbohydrates and timing ofconsumption), such meal information may be considered as user input. Insome embodiments, user input may come from another electronic device(e.g., via remote monitoring from a parents' smart phone, or the like).

In some embodiments, transitioning (1085) from the acknowledged state(1020) to the inactive state (1030) is based on the sensor data nolonger meeting one or more active transition criteria associated with ahypoglycemic condition or hyperglycemic condition and/or based one ormore inactive transition criteria (e.g., that may be different from theone or more active transition criteria associated with the ahypoglycemic condition or hyperglycemic condition (e.g., associated withthe initial alert). Additionally or alternatively, transitioning (1080)from the acknowledged state to the inactive state may be based oninsulin data and/or meal information. In some embodiments, transitioning(1090) includes a time element, for example, after the expiration of anacknowledgement (active monitoring) time period, which may be fixedand/or user settable.

Inactivation criteria for transitioning from acknowledged to inactivemay be similar to or the same as conditions for inactive to active(except that acknowledged state would be entered when the one or morefirst or second criteria (e.g., see FIG. 6) were not met and/or fortransitioning from active to inactive. In some embodiments, certain ofthe following inactivation criteria or conditions may apply for a statetransition from acknowledged to inactive based on a hyperglycemiccondition: the average glucose level over a predetermined time period(e.g., 10, 20, 30, 40 minutes) is less than a predetermined threshold(e.g., 160, 180, 200, 220 mg/dL); minus a predetermined delta (e.g., 10,15, 20 mg/dL); the acknowledge time period has expired and the glucoselevel is less than a predetermined threshold (e.g., 160, 180, 200, 220mg/dL); and/or the rate of change of glucose is dropping at apredetermined rate.

Alternatively, the inactivation criteria for transitioning fromacknowledged to inactive may be different (e.g., more stringent). Inaddition to sensor data, other data including insulin data and/or userinput may be considered for the state transition criteria. In someembodiments, the following inactivation criteria or conditions may applyfor a state transition from acknowledged to inactive based on ahypoglycemic condition may include determining whether the glucose valuehas increased by more than a predetermined amount (e.g., 10, 15, 20mg/dL, which may be indicative of some user action) and whether theglucose value has risen above the threshold condition (e.g., from thefirst function). Inactivation conditions for the real-time alert (firstfunction) and predictive alert (second function) may be the same ordifferent. In some embodiments, a positive glucose rate of change(increasing rise rate) exceeding a certain value may be used as acondition because it may be assumed that the rise rate is indicative ofthe user taking some preventive action. In some embodiments, the riserate condition may be combined with a glucose value threshold condition.In some embodiments, the glucose value rise rate condition is 0.25, 0.5,or 1 mg/dL/min.

In some embodiments, in cases where a hypoglycemic condition hastriggered an active state, and active monitoring after useracknowledgement, the state may transition from acknowledged state toinactive state prior to the expiration of the acknowledgement timeperiod based on inactivation criteria or data indicative of hypoglycemicconditions significantly improving for example, a determination that thehost's glucose level is greater than the low threshold plus a delta andthe predicted glucose level for the prediction horizon is more than somepredetermined limit (which may be the same or different from thatprediction horizon or threshold of initial activation).

In some embodiments, transitioning (1060) from the acknowledged state(1020) to the active state (1010) is based on the one or more activationcriteria associated with a hypoglycemic condition or hyperglycemiccondition being met and based on an expiration of a predetermined timeperiod. Activation criteria for transitioning from acknowledged toactive may be any of the same conditions for initially alerting or maybe different (e.g., based on glucose trending away from euglycemia). Inaddition to sensor data, other data including insulin data and/or userinput may be considered for the state transition criteria. Additionallyembodiments discussed herein with respect to reactivation may be appliedhere.

In some embodiments, reactivation criteria for transitioning (1065) fromacknowledged state (1020) to active state (1010) may be different thancriteria for initially transitioning to active state. For example, evenwhen the user has acknowledged the alert (during the active monitoringand/or acknowledged time period), a worsening condition may indicate aneed for a re-alert based on sensor data indicative of worseningglycemic conditions, such as the second function meeting one or morecriteria and/or the sensor data indicative of glucose further trendingaway from euglycemia based one or more criteria indicative of worsening.

In some embodiments, the following reactivation criteria or conditionsmay apply for a state transition from acknowledged to active based on ahypoglycemic condition after acknowledged time has expired: glucoselevel is less than a predetermined threshold (e.g., 80, 60, 60 mg/dL);glucose level is than a predetermined threshold and rate of change isless than is a predetermined rate (e.g., 1.0 mg/dL/min, e.g., not risingrapidly); or glucose level is predicted to go below a second threshold(e.g., 55 mg/dL) within a prediction horizon (e.g., 10, 15, 20 minutes).However, in some embodiments, reactivation criteria indicative of aworsening hypoglycemic condition may case a state transition fromacknowledged to active prior to expiration of the acknowledged timeperiod (re-activation or re-alert) based on one or more re-alertcriteria (e.g., different from criteria for initial alert), some ofwhich are described in more detail elsewhere herein. In someembodiments, an alert activated by the first function (at 520) maytrigger a re-alert or re-activation during the acknowledgement timeperiod (transition to active prior to expiration of time period) if thesecond function meets the second criteria (at 530).

In some embodiments, the activation criteria associated with ahyperglycemic condition to transition from acknowledged to active mayinclude a determination of whether: the glucose level exceeds apredetermined threshold (e.g., 160, 180, 200, 220 mg/dL); the averageglucose level over a predetermined time period (e.g., 10, 20, 30, 40minutes) is more than a predetermined threshold (e.g., 160, 180, 200,220 mg/dL); the current glucose level is more than a predeterminedthreshold plus a predetermined margin (e.g., 25, 50, 75 mg/dL); and/orthe rate of change of glucose is greater than a predetermined threshold(e.g., −0.5 mg/dL per minute).

In some embodiments, transitioning (1065) from the acknowledged state(1020) to the active state (1010), also referred to as reactivation,occurs after determining data is indicative of the host's glucosetrending toward euglycemia, also referred to as the recoveringsub-state, and the subsequently trending away from euglycemia during theactive monitoring time period (based on one or more criteria). In otherwords, a rebound situation may occur, after an active alert, when auser's glycemic condition initially trends towards euglycemia(recovering), but subsequently trends back towards the hyper- orhypoglycemic condition. Accordingly, reactivating the first alert stateduring the acknowledgement time period may be by the data associatedwith the host's hypoglycemic or hyperglycemic condition meeting one ormore rebound (e.g., reactivation) criteria indicative of a reboundsituation. In general, criteria for transitioning from recovering toactive may be any of the same conditions for initially alerting or maybe different (e.g., a reversing trend of glucose value, rate of changeor acceleration). In some embodiments, the one or more rebound criteriainclude conditions indicative of the host's glucose trending towardeuglycemia (e.g., recovering sub-state) and subsequent conditionsindicative of the host's glucose trending away from euglycemia duringthe active monitoring time period. In addition to sensor data, otherdata including insulin data and/or user input may be considered for thestate transition criteria. Additionally embodiments discussed hereinwith respect to reactivation may be applied here.

In some embodiments, transitioning (1070) from active state (1010) toinactive state (1050) may be any of the same conditions for initiallyalerting (except that inactive state would be entered when the one ormore first or second criteria (of FIG. 6) were not met). Alternatively,the inactivation criteria for transitioning from active to inactive maybe different (e.g., different thresholds). In addition to sensor data,other data including insulin data and/or user input may be consideredfor the state transition criteria. In one example, a transition fromactive to inactive is based on the alert conditions no longer being met,e.g., EGV and delta EGV as described in more detail elsewhere herein.

In some embodiments, certain of the following inactivation criteria orconditions may apply for a state transition from active to inactivebased on a hyperglycemic condition: the average glucose level over apredetermined time period (e.g., 10, 15, 30, 45 minutes) is less than apredetermined threshold (e.g., 160, 180, 200, 220 mg/dL) minus apredetermined delta (e.g., 10, 15, 20 mg/dL); the glucose level over apredetermined time period (e.g., 10, 15, 30, 45 minutes) goes below apredetermined threshold (e.g., 160, 180, 200, 220 mg/dL) minus apredetermined delta (e.g., 10, 15, 20 mg/dL); the average glucose levelover a predetermined time period is less than a predetermined threshold;and/or the rate of change of glucose is falling (negative) at greaterthan a predetermined rate (e.g., 1 mg/dL/min).

In some embodiments, certain of the following inactivation criteria orconditions may apply for a state transition from active to inactivebased on a hypoglycemic condition: the average glucose level over apredetermined time period (e.g., 10, 15, 30, 45 minutes) is greater thana predetermined threshold (e.g., 60, 70, 80 mg/dL) plus a predetermineddelta (e.g., 10, 15, 20 mg/dL); the glucose level over a predeterminedtime period (e.g., 10, 15, 30, 45 minutes) goes above a predeterminedthreshold (e.g., 60, 70, 80 mg/dL) plus a predetermined delta (e.g., 10,15, 20 mg/dL); the average glucose level over a predetermined timeperiod is more than a predetermined threshold; and/or the rate of changeof glucose is increase (positive) at greater than a predetermined rate(e.g., 1 mg/dL/min).

For example, in some embodiments, the criteria to get out of theacknowledged state, where the acknowledgement is based on data useracknowledgement (e.g., pressing button), to either an inactive state oractive state (e.g., reactivation) are different from the criteria to getout of acknowledged state, wherein the acknowledgement is based on useraction detected (e.g., recovery detected by monitoring of data). Thisacknowledgement based on user action detected is described in moredetail below in the acknowledgement sub-state recovery discussion. Forexample, to go from the acknowledged state, where the acknowledgement isbased on data user acknowledgement, to the inactive state, certain firstcriteria may apply (e.g., criteria that allow for oscillations aroundthe threshold without inactivation occurring, or, in other words,criteria that ensure the user is not merely oscillating around thethreshold). In contrast, to go from the acknowledged state, where theacknowledgement is based on user action detected (recovery sub-state),to the inactive state, certain second criteria may apply (e.g., thesecond criteria do not consider oscillations, but rather consider thedata confirming the user's detected successfully recovered intoeuglycemia.

As mentioned above, the conditions or criteria for reactivation may bemore stringent than for the initial activation. Similarly, thereactivation criteria to get out of the acknowledged state, where theacknowledgement is based on user acknowledgement (e.g., pressing abutton), to the active state may be different from the reactivationcriteria to get out of acknowledged state, wherein the acknowledgementis based on user action detected (e.g., recovery detected by monitoringof data). In some embodiments, the reactivation criteria for thetransition from acknowledged (based on user acknowledgement) to active,may be time-based or could include a stringent second set of criteria(e.g., change greater than 200 mg/dL). In contrast, the reactivationcriteria for the transition from acknowledged (based on user actiondetected, recovery sub-state) to active, may include recognizing areversing trend or a worsening, etc. indicative of rebound conditions,based on monitoring of the sensor data. Example worsening of conditionsmay include or be based on a strong change in glucose away from atarget, and could be based on glucose level (g), amount of glucosechange (Δg), rate of glucose change (Δg/t), rate of acceleration, orcombinations thereof.

Referring back to FIG. 8, at block 730, processor module 214 may beconfigured to process a response to an alert state change or transition.In some embodiments, processor module 214 may include instructions orcriteria on how to process an appropriate response for a status change.In some embodiments, such responses may be stored in e.g., a lookuptable, or the like. In some embodiments, the output associated with atransition to the active state is different from the output associatedwith a transition from the acknowledged state to the active state and/ordifferent from a transition from the acknowledged state to an inactivestate.

It should be appreciated that depending on the status change there maybe one or more possible and/or appropriate responses. For example, ifsensor data indicates that a user has stabilized his glucose value andcome down sufficiently from a hyperglycemic event (e.g., active toinactive state transition), a particular type of alert indicating apositive status change or kudos may be provided. The kudos may have aparticular sound, e.g., particular pitch or number of chimes. In someembodiments, the user may be able to customize or select the alertsignature, similar to that currently available to smart phone users.

In other embodiments, a particular type of alert indicating a negativestatus change or warning may be provided (e.g., transitioning back toactive state). The warning may also have a particular sound and may, insome instances, be customizable by the user. In some embodiments, theseverity of the warning may be reflected by the sound itself of thewarning, e.g., the warning may be louder or more intense or maybe soundsimilar to a recognizable distress sound. In some embodiments, are-alert or follow up alarm may be different than the initial alert.

State transitions are described in more detail elsewhere herein;however, it should be appreciated that any of a variety of indicatorsmay be provided audibly, tactilely, visually and/or communicated viadata transfer, based on preset or user selectable options.

At block 740, processor module 214 is optionally configured to outputinformation associated with the state transition, for example, toprovide an alert or alarm or kudos if warranted by the detection of astate or status change. In some embodiments, wherein the outputassociated with a transition to the active state is different from theoutput associated with a transition from the acknowledged state to theinactive state and/or active (reactivation) state. In some embodiments,the alert or alarm or kudos may be subject to a time restriction, e.g.,such as if the user has pushed an acknowledgement on alerts. This maydisable the user from receiving alerts/alarms/kudos in all but a handfulof situations, such as where the alarm is characterized as areactivation condition, as described in more detail with reference toFIG. 9.

Some use cases arise, wherein immediate notification of an alert may notbe advantageous. For example, post-prandial glycemic excursions arenormal for people with diabetes, even with insulin injections, or peoplewithout diabetes. In some circumstances, alerting a user about glycemicexcursions of which they are already aware may lead to frustration anddesensitization of the alarms. It may be preferable to wait anddetermine whether the host's glycemic excursion follows a normal courseof recovery before alerting them. Accordingly, in some embodiments, theprocessor module may be configured to provide output associated with afirst alert state after waiting a predetermined time period, wherein theoutput is based on the data associated with the host's hyperglycemiccondition meeting one or more second criteria after the predeterminedwaiting time period. Thus, it would follow that the processor module 214may be configured to not provide output associated with the first alertstate after the waiting time period based on the data associated withthe host's hyperglycemic condition not meeting the one or more secondcriteria, thereby allowing the state to transition to the active stateand back to the inactive state without alerting and/or otherwiseproviding output to a user. In these embodiments, the one or more firstcriteria and the one or more second criteria may be the same may bedifferent and the waiting time period may be fixed or user selectable.For example, the one or more second criteria may include determinewhether: the glucose level exceeds a predetermined threshold (e.g., 160,180, 200, 220 mg/dL); the average glucose level over a predeterminedtime period (e.g., 10, 20, 30, 40 minutes) is more than a predeterminedthreshold (e.g., 160, 180, 200, 220 mg/dL); the current glucose level ismore than a predetermined threshold plus a predetermined margin (e.g.,25, 50, 75 mg/dL); estimated time to a threshold (e.g., based on rate ofchange of glucose) and/or the rate of change of glucose is greater thana predetermined threshold (e.g., −0.5 mg/dL per minute).

FIG. 9 is a flowchart 800 illustrating an example process fordetermining when to re-alert and/or for determining a state transitionpost-alert activation from acknowledged back into an active state, alsoreferred to as reactivation conditions, in accordance with an embodimentof the disclosure. In some embodiments, reactivation conditions havedifferent criteria for going into an active alert state (providing alertoutput) as compared to an initial trigger of the active alert state,e.g., as described with reference to FIG. 6, in order to avoidunnecessary “flickering” re-alerts.

At block 810, processor module 214 may be configured to determine if oneor more sensor data points is within a predetermined or predefined zoneduring a predetermined time period post-alert trigger, in one exampleembodiment. Such predetermined or predefined zone may include a regionabove and/or below the threshold that triggered the initial alertprovided to the user. For example, referring back to FIG. 7, TV_(Z1) andTV_(Z2) may provide a buffer zone or envelope above and below athreshold value. In some embodiments, the predefined zone is a zone thatdefines a range surrounding a value that triggered the first alert. Forexample, the predefined zone may be 10 mg/dL above the value thattriggered the first alert and/or 10 mg/dL below the value that triggeredthe first alert.

In some embodiments, a data point in a predefined zone is indicative ofno status change. In such embodiments, no further action is performed.In embodiments utilizing state transitions, assuming the user hasacknowledged the alert/alarm (or based on data analysis), theacknowledged alert state remains and no transitions to either inactiveor active will occur. In contrast, if a sensor data point moves outsidethe zone, the state remains in acknowledged and may be activelymonitored (e.g., for recovery or worsening based on certain criteriaindicative of the host's glucose level trending towards or away fromeuglycemia), or for transition to inactive if the sensor data movestowards euglycemia based on a second (more stringent that first) one ormore criteria, or transitions to active (e.g., reactivation if thesensor data moves away from euglycemia based on additional one or morereactivation criteria). For example, if the user transitions from theacknowledged state to the active state, the user may be re-alerted.

At block 820, processor module 214 also may be optionally configured todetermine if a user has acknowledged an alert, as described in moredetail elsewhere herein, whereby alerts or alarms are not provided to auser for a set time period, e.g. 30 minutes. In some embodiments,processor module 214 may be configured to determine if a user has takensome sort of action independent of the sensor during the predeterminedtime period post-alert trigger. For example, the user may have eaten orincreased insulin since the initial alarm, which would likely lead to anoticeable change in glucose levels. The user may or may not input thistype of information on the sensor. However, certain types of changes,e.g., insulin update, may have identifiable patterns that the processormodule 214 may be able to determine was a user action, such as a changein glucose level, direction, rate of change, oracceleration/deceleration.

At block 830, processor module 214 may be configured to determine if areactivation condition has been met by one or more sensor data points.As used herein, a “reactivation condition” refers to a condition thatwould cause an alert or alarm to be provided to the user after theinitial alert, during the “acknowledged time period” when a user isn'texpected to receive additional unnecessary alerts (e.g., a statetransition from the acknowledged state to active state). Generallyspeaking, the reactivation condition will more likely than not relate toalarms for events considered to be dangerous to a user's health, e.g., asevere hypoglycemic event. In some embodiments, a reactivation conditionmay be thought of as a condition or event that would cause a host oruser to change from acknowledged into an active state, as discussed withrespect to FIG. 11.

Additionally, because the reactivation may have been unexpected orresponsive to an attempt to suppress, the output associated with thereactivation may be more noticeable or different from other alarms, forexample, by displaying explanatory information or questions of actionstaken and/or by escalating the alarms as may be appreciated by oneskilled in the art.

FIG. 10 is a flowchart 900 illustrating an example process fordetermining if a reactivation condition is met in accordance with anexample embodiment of the disclosure. At block 910, processor module 214may be configured to determine if one or more sensor data points isoutside a predetermined or predefined zone. For example, as shown inFIG. 7, there may be regions or zones that data points seem to oscillatearound after an alert has been provided to a user. Once a data point isdetermined to fall outside this predefined zone, it may be indicative ofa user's glucose level moving toward an undesirable value or in anundesirable trend (e.g., trending away from euglycemia).

At block 920, processor module 214 may be configured to determine,depending on whether the data point has a high or low value, e.g. iswithin a range of the high or low threshold indicative of ahyperglycemic event or a hypoglycemic event, respectively, whatdirection and/or rate the data is moving or trending. Additionally, insome embodiments, the processor module 214 may be further configured toconsider user input and/or insulin information as variables in assessingwhether or not a reactivation condition has been met.

As may be appreciated, having a reactivation condition may also bethought of as intelligent acknowledged conditions, because it limits thenumber of alerts that may be provided to the user following a firstalert. This can help ensure that a user is not annoyed by a series ofadditional alerts going off after the first alert, but allows for moreintelligence and safety than a simple time-based snooze. In someembodiments, data points that may be considered to qualify asreactivation conditions may be predetermined by or fixed by factorysettings. Such reactivation conditions may be stored, for example, in alookup table.

In some embodiments, reactivation is intended to re-alert users whenthey have taken an action that is inadequate and returned to a dangerouszone. For example, after a low threshold alert the user may eatsomething that starts to increase their glucose but it is not enough sotheir glucose drops again. It is desirable to alert the user again inthis situation, but it should be distinguishable fromoscillating/annoying alarms. This may be achieved by e.g., settingcriteria of glucose rising some distance above the low threshold andthen falling below, or rate of change increasing to e.g., above 1mg/dL/min and then going negative again.

In some embodiments, following the first or initial alert to the user,the user may suspend additional alerts by acknowledging the initialalert. For example, when a user or host acknowledges the initial alert,additional alerts are suspended for a time period, except for re-alertconditions. In other words, following a threshold or predictive alert,the user would not hear another threshold or predictive alertimmediately after, but would instead wait for a time period, e.g., 30minutes. In some embodiments, the suspend time is user settable. In someembodiments, the user may have a default of 30 minutes and a maximum ofe.g., 2 hours for safety.

In some embodiments, a post-alert set of instructions or algorithms maybe used to detect the end of a hypoglycemic and/or hyperglycemic eventtransition to a recovering sub-state such that re-alert only occurs ifanother event occurs (e.g., rise of 5 mg/dL about threshold, upwardslope greater than 1 mg/dL/min, etc.). In such embodiments, this canhelp prevent an annoying situation of stable glucose oscillating around80 mg/dL and setting off threshold alert over and over, as describedabove.

In some embodiments, alerts may be generated if the user remains in thesame condition, e.g., just below 70 mg/dL, for a long period of timebecause the length of time hypoglycemic may also be considered a healthrisk.

EXAMPLES

A number of examples are provided below. When referring to a “low”threshold, it should be understood that the low threshold relatesgenerally to a low glucose value that may be indicative of ahypoglycemic event. Similarly, when referring to a “high” threshold, itshould be understood that the high threshold relates generally to a highglucose value that may be indicative of a hyperglycemic event. As usedherein, the glucose values may be estimated glucose values (EGVs) or anyknown type of glucose indicator or sensor data.

It should be appreciated that the following examples may be implementedaccording to the flow charts described above. The examples relate tospecific implementations and are provided for a more in-depthunderstanding of how the disclosed processes can operate. The examplesshould not be construed as limiting, but rather as a general guide onhow certain aspects may be performed.

Example 1 Low Threshold Alert

In this example, the processor module may be configured to receivesensor data (510) and evaluate the sensor data using a first function(520) to determine whether the “real-time” glucose value has crossed afirst threshold (80 mg/dL) and using a second function (530) todetermine whether the predicted glucose value will cross a secondthreshold (55 mg/dL) within 15 minutes (PH). The processor module may beconfigured to actively monitor data (710) associated with glycemiccondition after a hypoglycemic indicator has been triggered based on thefirst or second function to determine whether a state change transitionshould occur. In this example, the following three inactivationconditions exist (to transition from active or acknowledged toinactive): 1) if the glucose value increases by more than apredetermined amount (15 mg/dL) and the glucose value is above the firstthreshold (80 mg/dL) since activation of the hypoglycemic indicator; 2)the rate of change of glucose is increasing at a rate greater than apredetermined value (1 mg/dL/min); and 3) the acknowledged time periodis over (30 minutes).

Scenario A—Glucose Level is More than a Low Threshold

In this scenario, a hypoglycemic indicator is triggered based on thefirst function (host's glucose goes below 80 mg/dL), and the user isalerted via a user interface (visually and audibly). The useracknowledges the alert by pressing a button and the state transitions tothe acknowledged state. In the acknowledged state, the processor moduleactively monitors the host's glycemic condition as the glucose valuegoes down to 65 mg/dL, then starts coming back up and reaches 75 mg/dL,but then starts falling again. In this example, the acknowledged statewill remain for 30 minutes during which the user will not receive anyadditional alerts, during active monitoring the processor moduledetermined the user's glucose to be recovering (coming back up andreaching 75 mg/dL), which triggered the recovering sub-state ofacknowledged. The user will not be alerted until his/her glucose reachesa reactivation or inactivation condition. In the acknowledged state,recovering sub-state, criteria for re-alert may include any conditionindicative of a glucose “rebound.” Advantageously, the recoveringsub-state takes advantage of the fact that some user interaction wasdetected, thus it can be assumed that the user has attempted to treathim/herself and should be re-alerted only if the user's action was notsufficient and a rebound (e.g., predetermined reversal of trend orworsening of glucose) is detected. In this scenario, the statetransitioned back to active when the glucose started falling again(i.e., based on reactivation conditions), and the user was re-alerted.

Scenario B—Glucose Level Oscillating Around the Threshold

In this scenario, a hypoglycemic indicator is triggered based on thefirst function (host's glucose goes below 80 mg/dL), and the user isalerted via a user interface (visually and audibly), but this time thehost's glucose stays in the range 70-90 mg/dL going above and below 80mg/dL several times. Once the user acknowledges the alert, theacknowledged state is maintained and the user will not get any alertsuntil the acknowledged time is over. In other words, the states do nottransition back and forth from active to inactive, causing re-alerts asthe user oscillates above and below 80 mg/dL. Advantageously, thisavoids annoying alerts by allowing for some buffer to avoid flickeringof the alert off and on. If the user does not acknowledge, there may bere-alerts every 5 minutes (maintains in active state until acknowledgedby the user). However, it is important to note that the alert is not aconventional “threshold” alert. Instead, it is an “early warning” alertand as a result warning the user of the condition is reasonable. Afterthe acknowledged time is expired, the state will transition to either oractive or inactive depending on whether the alert conditions are stillmet. The state will transition to active and the alert may be shown onlyif the EGV is still below the threshold of 80 mg/dL (e.g., if theacknowledged time is 30 minutes and after 30 minutes, the EGV is 75mg/dL, the user will get an alert). The state will transition toinactive and the user will not get an alert if the EGV is 85 mg/dL(unless the fall rate predicted is enough to trigger the alert).

Scenario C—Prediction Followed by Crossing the Threshold

In this case, the user's glucose value is 100 mg/dL, and is predicted toreach 55 mg/dL in 15 minutes as determined by the second function (530).The active state is triggered and the alert is displayed to the user(according to block 550). Once the user acknowledges, the acknowledgedstate is maintained until the glucose value falls to 80 mg/dL (the lowthreshold) in 10 minutes, at which time the state remain as acknowledgedas the host's glucose remains around 80 mg/dL for 20 minutes. Notably,no alert will be triggered when 80 mg/dL is reached (acknowledged statemaintained), the state will transition back to active after 30 min ifthe threshold condition is met.

Example 2 Actionable Alerts Associated with Hyperglycemic Conditions

Advantageously, the user may be alerted only when it is likely that auser action is necessary. In one such example, the user may still bealerted as soon as the glucose value crosses an alert threshold.However, a user may also select to enable the wait time under certainconditions, for example, when a hyperglycemic excursion is correlatedwith known meal ingestion. In some embodiments, whether or not the waittime is enabled, annoying repeated alerts when the glucose valueoscillates around the threshold may be minimized or eliminated, whileensuring that if the glucose value crosses the threshold twice due toreasons that are likely not related, then the user is alerted. Forexample, the user may be alerted once after the glucose value goes highdue to carbohydrate intake (hyperglycemic alert) or not at all. Then theuser takes insufficient amount of insulin, which causes the glucosevalue to go down a little bit, but comes back up once more.

FIG. 12 is an example graph showing average EGV is high over last Tminutes. Notably, the user's glucose level crossed the predeterminedhyperglycemic threshold at the beginning of T minutes, and the processormodule transitioned to the active state 700 based on the (first) activetransition criteria of the glucose level exceeding a first predeterminedhyperglycemic threshold level of 180 mg/dL, thereby triggering thedynamic and intelligent monitoring of the host's glycemic condition.However, the user was not alerted because the user had enabled apredetermined wait time period (T) of 60 minutes before high alert.During the 60 minutes, the processor module actively monitored thehost's glycemic condition using one or more hyperglycemic (second)criteria based on the average glucose over the predetermined wait timeperiod, after which the glucose level was determined to be greater thanthe predetermined threshold level (180 mg/dL), resulting in outputassociated with the alert at 1200. In this case, the active state wasinitiated based on the first criteria (glucose level exceeding athreshold), but the alert was not provided to the user until the secondcriteria was met (based on average glucose level over the wait timeexceeding a threshold).

FIG. 13 is example graph showing average glucose is high, but fallingrapidly, thus no alert is provided to the user or host because, althoughthe average glucose level is high, the glucose level is falling rapidly.Notably, the user's glucose level crossed the predeterminedhyperglycemic threshold at the beginning of T minutes, and the processormodule transitioned to the active state 700 based on the (first) activetransition criteria of the glucose level exceeding a first predeterminedhyperglycemic threshold level of 180 mg/dL, thereby triggering thedynamic and intelligent monitoring of the host's glycemic condition.However, the user was not alerted because the user had enabled apredetermined wait time period (T) of 60 minutes before high alert.During the 60 minutes, the processor module actively monitored thehost's glycemic condition using one or more hyperglycemic (second)criteria based on the rate of change of the glucose level, which wasdetermined to be dropping a rate greater than the predetermined value (1mg/dL/min), resulting in no output at 1300. In this case, the activestate was initiated based on the first criteria (glucose level exceedinga threshold), but the processor module determined a state transition toinactive at 1300.

In some embodiments, the activation condition may include a timecriteria or time component, meaning that a user's glucose has to exceeda threshold on average over a predetermined period of time, e.g., 60minutes. As applied to the example shown in FIG. 13, the processormodule may actively monitor the host's glycemic condition continuouslyusing one or more hyperglycemic criteria based on the average glucoselevel and rate of change of the glucose level. In this scenario at 1300,the average glucose over 60 minutes was found to be above apredetermined threshold, however the rate of change of the glucose wasdetermined to be dropping a rate greater than the predetermined value (1mg/dL/min), resulting in no output at 1300. Consequently, the activestate is not entered in this circumstance and no output is provided tothe user.

FIG. 14 is an example graph showing a high glucose level, followed by arapid rate of change down, but then a steady glucose level still abovethe predetermined threshold. Notably, the user's glucose level crossedthe predetermined hyperglycemic threshold at the beginning of T minutes,and the processor module transitioned to the active state 700 based onthe (first) active transition criteria of the glucose level exceeding afirst predetermined hyperglycemic threshold level of 180 mg/dL, therebytriggering the dynamic and intelligent monitoring of the host's glycemiccondition. However, the user was not alerted because the user hadenabled a predetermined wait time period (T) of 60 minutes before highalert. During the 60 minutes, the processor module actively monitoredthe host's glycemic condition using one or more hyperglycemic (second)criteria based on the glucose level and the rate of change of theglucose level, which at the end of the waiting period, was determined tostill exceed the predetermined threshold and to have a rate of changethat was not dropping faster than a predetermined rate, resulting inoutput at 1400. In this case, at reference numeral 1400, the user orhost should be alerted here because although the glucose level fell forsome time, the glucose level was steady and high (e.g., above athreshold) at the end of the wait time.

FIG. 15 is an example graph showing a host's glucose level that goesbeyond the threshold plus a margin even before the wait time expires.Notably, the user's glucose level crossed the predeterminedhyperglycemic threshold at the beginning of T minutes, and the processormodule transitioned to the active state 700 based on the (first) activetransition criteria of the glucose level exceeding a first predeterminedhyperglycemic threshold level of 180 mg/dL, thereby triggering thedynamic and intelligent monitoring of the host's glycemic condition.However, the user was not alerted because the user had enabled apredetermined wait time period (T) of 60 minutes before high alert.During the 60 minutes, the processor module actively monitored thehost's glycemic condition using one or more hyperglycemic (second)criteria based on the glucose level plus a margin (180 mg/dL+50mg/dL=230 mg/dL), which occurred prior to the end of the waiting period,resulting in output at 1500 prior to the 60 minute expiration. In thiscase, the user or host should be alerted at 1500 because the glucoselevel has risen to such an extent that the no-alert waiting periodshould be overridden.

FIG. 16 is an example graph showing after user acknowledgement of analert in a scenario similar to FIG. 14, because although the glucoselevel fell for some time, the glucose level was steady and high (e.g.,above a threshold) at the end of the wait time. After the alert wasoutput at 1600, the user acknowledged the alert at 1610, transitioninginto the acknowledged state, which has a 60 minute active monitoringperiod. Notably, the user's glucose level crossed below thepredetermined hyperglycemic threshold soon after the user acknowledgedthe alert, however not sufficiently low to inactive the alert (becausethe inactivation conditions included threshold plus delta criteria).Accordingly, the processor module continued to actively monitor thehost's glycemic condition based on average glucose, which determined theaverage glucose level at something above the predetermined threshold atthe end of 60 minutes. In this case, the user or host is re-alerted orprovided a subsequent alert because the average EGV is still high afterthe acknowledgment time period 1620.

The scenarios above assume that “enable wait before alert” is turned on,however, any of those scenarios would have functioned similarly if thewait time was not enabled, but the acknowledged time period was selectedby the user to be 60 minutes, in which case user would have a firstalert after the first criteria were met (at the beginning of time periodT) and then another one after the second criteria were met, for exampleat 1200, 1400, 1500 or 1600 (but not at other times in between when theuser's glucose level oscillated up and down around the predeterminedthreshold).

This example further describes how the processor module activelymonitors the host's glycemic condition after the user acknowledges ahyperglycemic alert (e.g., triggered with the host's glucose meeting oneor more first or second criteria). Subsequent to the hyperglycemic alertoutput, the user acknowledges the alert, thus it may be assumed that theuser is either watching or taking some action. The processor moduletransitions to acknowledged state. The processor module then activelymonitors the host's glycemic condition, such that if the host's averageglucose level since activation of the alert is less than the threshold(180 mg/dL) minus a delta (15 mg/dL); the host's actual (real-time)glucose level is less than the threshold minus the delta (165 mg/dL);average glucose level since activation of the alert is less than thepredetermined threshold and the acknowledged time has expired; or rateof change of the host's glucose level falls faster than a predeterminedrate (e.g., 1.0 mg/dL/min) and the glucose level is less than athreshold, predetermined meal information is received or confirmed; orpredetermined insulin delivery information is received or confirmed,then the processor module would transition to inactive based on thoseinactivation criteria.

The disclosure presents the best mode contemplated for carrying out thepresent systems and methods for processing analyte sensor data, and ofthe manner and process of practicing them, in such full, clear, concise,and exact terms as to enable any person skilled in the art to which theypertain to practice these systems and methods. These systems and methodsare, however, susceptible to modifications and alternate constructionsfrom those discussed above that are fully equivalent.

For example, it should further be appreciated that the implementationand/or execution of all methods and processes may be performed by anysuitable devices or systems, whether local or remote. Further, anycombination of devices or systems may be used to implement the presentmethods and processes. Additionally, in some embodiments, the methodsand processes described herein may reside in a non-transitorycomputer-readable storage medium including code which when executed byat least one processor causes operations of the present disclosure to berealized.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. The functions, steps and/oractions of the method claims in accordance with the embodiments of theinvention described herein need not be performed in any particularorder. Furthermore, although elements of the invention may be describedor claimed in the singular, the plural is contemplated unless limitationto the singular is explicitly stated. Therefore, the description andexamples should not be construed as limiting the scope of the disclosureto the specific embodiments and examples described herein, but rather toalso cover all modification and alternatives coming with the true scopeand spirit of the disclosure.

What is claimed is:
 1. A method of activating a hypoglycemic indicatorbased on continuous glucose sensor data, the method comprising:evaluating sensor data using a first function to determine whether areal time glucose value meets one or more user settable first criteria;evaluating sensor data using a second function to determine whether apredicted glucose value meets one or more non user settable secondcriteria; activating a hypoglycemic indicator if either the one or moreuser settable first criteria or the one or more non user settable secondcriteria are met; and providing an output based on the activatedhypoglycemic indicator.
 2. The method of claim 1, wherein the evaluatingsensor data using a first function to determine whether a real timeglucose value meets one or more user settable first criteria comprisesdetermining whether the real-time glucose value passes a glucosethreshold.
 3. The method of claim 2, wherein the evaluating sensor datausing a first function to determine whether a real time glucose valuemeets one or more user settable first criteria further comprisesdetermining whether an amplitude of rate of change or direction of rateof change meets a rate of change criterion.
 4. The method of claim 1,wherein the evaluating sensor data using a first function to determinewhether the real time glucose value meets one or more user settablefirst criteria comprises evaluating a static risk of a substantiallyreal time glucose value.
 5. The method of claim 1, wherein theevaluating sensor data using a second function to determine whether thepredicted glucose value meets one or more second criteria comprisesevaluating a dynamic risk of the predicted glucose value.
 6. The methodof claim 1, wherein the second function comprises an artificial neuralnetwork model that uses at least one of exercise, stress, illness orsurgery to determine the predicted glucose value.
 7. The method of claim1, wherein the second function utilizes a first order autoregressivemodel to determine the predicted glucose value.
 8. The method of claim7, wherein the first order autoregressive model comprises a parameteralpha, and wherein alpha is estimated recursively each time a sensordata points is received.
 9. The method of claim 1, wherein evaluatingthe sensor data using the first function and the second function allowsfor increased warning time of hypoglycemic alerts without substantiallyincreasing the number of alerts as compared to evaluating the sensordata using the first function without evaluating the sensor data usingthe second function.
 10. The method of claim 1, wherein the secondfunction comprises a Kalman Filter to determine the predicted glucosevalue using as an input an estimate of the rate of change of the bloodglucose.
 11. The method of claim 1, wherein the one or more usersettable first criteria comprises a first threshold that is configuredto be user settable.
 12. The method of claim 11, wherein the one or morenon user settable second criteria comprises a second threshold that isnot user settable.
 13. The method of claim 1, wherein the one or morenon user settable second criteria comprises a prediction horizon. 14.The method of claim 1, wherein the one or more non user settable secondcriteria comprises a second threshold that is adaptively set by aprocessor module based on the one or more user settable first criteria.15. The method of claim 1, wherein the one or more non user settablesecond criteria comprises a prediction horizon that is adaptively set bya processor module based on the first user settable criteria.
 16. Themethod of claim 1, wherein the hypoglycemic indicator comprises a flagthat has a particular set of instructions associated with it dependingon whether the hypoglycemic indicator was activated based on the firstfunction meeting the one or more user settable first criteria or whetherthe hypoglycemic indicator was activated based on the second functionmeeting the one or more non user settable second criteria.
 17. Themethod of claim 1, wherein the output comprises at least one of anaudible, tactile or visual output, and wherein the output isdifferentiated and/or provides information selectively based on whetherthe hypoglycemic indicator was activated based on the first functionmeeting the one or more user settable first criteria or whether thehypoglycemic indicator was activated based on the second functionmeeting the one or more non user settable second criteria.
 18. Themethod of claim 1, wherein providing an output comprises transmitting amessage to an insulin delivery device including instructions associatedwith at least one of: a) suspending insulin delivery, b) initiating ahypoglycemia and/or hyperglycemia minimizer algorithm, c) controllinginsulin delivery or d) information associated with the hypoglycemiaindicator.
 19. The method of claim 1, wherein the one or more non usersettable second criteria are programmed at the factory in such a waythat no user is able to modify the second criteria.
 20. The method ofclaim 1, wherein the user is a host or a healthcare provider.
 21. Themethod of claim 1, wherein the second function provides no more than oneadditional alarm per week based on a retrospective analysis comparingthe use of the first function and the second function together ascompared to the first function alone.
 22. The method of claim 1, whereinevaluating the sensor data using the first function and the secondfunction provides increased warning time of hypoglycemic alerts withoutsubstantially increasing the number of alerts as compared to evaluatingthe sensor data using the first function without evaluating the sensordata using the second function.
 23. A method of activating ahypoglycemic indicator based on continuous glucose sensor data, themethod comprising: evaluating sensor data using a first function todetermine whether a real time glucose value meets one or more usersettable first criteria; evaluating sensor data using a second functionto determine whether a predicted glucose value meets one or more nonuser settable second criteria, wherein the second function utilizes afirst order autoregressive model to determine the predicted glucosevalue, wherein the first order autoregressive model comprises aforgetting factor, a prediction horizon and a prediction threshold tunedto provide no more than one additional alarm per week based on aretrospective analysis comparing the use of the first function and thesecond function together as compared to the first function alone;activating a hypoglycemic indicator if either the one or more usersettable first criteria or the one or more non user settable secondcriteria are met; and providing an output based on the activatedhypoglycemic indicator.
 24. A system for processing data, the systemcomprising: a continuous analyte sensor configured to be implantedwithin a body; and sensor electronics configured to receive and processsensor data output by the sensor, the sensor electronics including aprocessor configured to: evaluate sensor data using a first function todetermine whether a real time glucose value meets one or more usersettable first criteria; evaluate sensor data using a second function todetermine whether a predicted glucose value meets one or more non-usersettable second criteria; activate a hypoglycemic indicator if eitherthe one or more user settable first criteria or the one or more non usersettable second criteria are met; and provide an output based on theactivated hypoglycemic indicator.
 25. The system of claim 24, whereinthe evaluating sensor data using a first function to determine whether areal time glucose value meets one or more user settable first criteriacomprises determining whether the real-time glucose value passes aglucose threshold.
 26. The system of claim 25, wherein the evaluatingsensor data using a first function to determine whether a real timeglucose value meets one or more user settable first criteria furthercomprises determining whether an amplitude of rate of change ordirection of rate of change meets a rate of change criterion.
 27. Thesystem of claim 24, wherein the evaluating sensor data using a firstfunction to determine whether the real time glucose value meets one ormore user settable first criteria comprises evaluating a static risk ofa substantially real time glucose value.
 28. The system of claim 24,wherein the evaluating sensor data using a second function to determinewhether the predicted glucose value meets one or more non user settablesecond criteria comprises evaluating a dynamic risk of the predictedglucose value.
 29. The system of claim 24, wherein the second functioncomprises an artificial neural network model that uses at least one ofexercise, stress, illness or surgery to determine the predicted glucosevalue.
 30. The system of claim 24, wherein the second function utilizesa first order autoregressive model to determine the predicted glucosevalue.
 31. The system of claim 30, wherein the first orderautoregressive model comprises a parameter alpha, and wherein alpha isestimated recursively each time a sensor data points is received. 32.The system of claim 24, wherein evaluating the sensor data using thefirst function and the second function allows for increased warning timeof hypoglycemic alerts without substantially increasing the number ofalerts as compared to evaluating the sensor data using the firstfunction without evaluating the sensor data using the second function.33. The system of claim 24, wherein the second function comprises aKalman Filter to determine the predicted glucose value using as an inputan estimate of the rate of change of the blood glucose.
 34. The systemof claim 24, wherein the one or more user settable first criteriacomprises a first threshold that is configured to be user settable. 35.The system of claim 34, wherein the one or more non user settable secondcriteria comprises a second threshold that is not user settable.
 36. Thesystem of claim 24, wherein the one or more non user settable secondcriteria comprises a prediction horizon that is not user settable. 37.The system of claim 24, wherein the one or non user settable secondcriteria comprises a second threshold that is adaptively set by aprocessor module based on the one or more user settable first criteria.38. The system of claim 24, wherein the one or more non user settablesecond criteria comprises a prediction horizon that is adaptively set bythe processor module based on the one or more user settable firstcriteria.
 39. The system of claim 24, wherein the hypoglycemic indicatorcomprises a flag that has a particular set of instructions associatedwith it depending on whether the hypoglycemic indicator was activatedbased on the first function meeting the one or more user settable firstcriteria or whether the hypoglycemic indicator was activated based onthe second function meeting the one or more non user settable secondcriteria.
 40. The system of claim 24, wherein the output comprises atleast one of an audible, tactile or visual output, and wherein theoutput is differentiated and/or provides information selectively basedon whether the hypoglycemic indicator was activated based on the firstfunction meeting the one or more user settable first criteria or whetherthe hypoglycemic indicator was activated based on the second functionmeeting the one or more non user settable second criteria.
 41. Thesystem of claim 24, wherein providing output comprises transmitting amessage to an insulin delivery device including instructions associatedwith at least one of: a) suspending insulin delivery, b) initiating ahypoglycemia and/or hyperglycemia minimizer algorithm, c) controllinginsulin delivery or d) information associated with the hypoglycemiaindicator.
 42. The system of claim 24, wherein the one or more non usersettable second criteria are programmed at the factory in such a waythat no user is able to modify the non user settable second criteria.43. The system of claim 24, wherein the user is a host or a healthcareprovider.
 44. The system of 24, wherein the second function provides nomore than one additional alarm per week based on a retrospectiveanalysis comparing the use of the first function and the second functiontogether as compared to the first function alone.
 45. The system ofclaim 24, wherein the evaluation of the sensor data using the firstfunction and the second function provides increased warning time ofhypoglycemic alerts without substantially increasing the number ofalerts as compared to evaluation of the sensor data using the firstfunction without evaluation of the sensor data using the secondfunction.
 46. A system for processing data, the system comprising: acontinuous analyte sensor configured to be implanted within a body; andsensor electronics configured to receive and process sensor data outputby the sensor, the sensor electronics including a processor configuredto: evaluate sensor data using a first function to determine whether areal time glucose value meets one or more user settable first criteria;evaluate sensor data using a second function to determine whether apredicted glucose value meets one or more non-user settable secondcriteria, wherein the second function utilizes a first orderautoregressive model to determine the predicted glucose value, whereinthe first order autoregressive model comprises a forgetting factor, aprediction horizon and a prediction threshold tuned to provide no morethan one additional alarm per week based on a retrospective analysiscomparing the use of the first function and the second function togetheras compared to the first function alone; activate a hypoglycemicindicator if either the one or more user settable first criteria or theone or more non user settable second criteria are met; and provide anoutput based on the activated hypoglycemic indicator.